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Journal articles on the topic 'Real-time DoA estimation'
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1
Iwasaki, Nobuo, Katsuhiro Inoue, and Hiromu Gotanda. "A Real Time Oriented Sound Source DOA Estimation Based on Sparseness." Transactions of the Institute of Systems, Control and Information Engineers 27, no. 12 (2014): 493–500. http://dx.doi.org/10.5687/iscie.27.493.
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Liu, Fu Gang, and Ming Diao. "A Novel DOA Estimation Method for Wideband Signals." Advanced Materials Research 403-408 (November 2011): 2861–65. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2861.
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For direction of arrival (DOA) estimation of wideband signals, the traditional algorithms’ estimating accuracy and complexity were mainly affected by pre-estimation of DOA. By forming an angle set which elements were arithmetic progressions in the range of possible arriving angles, the algorithm used the new angle vector to replace the preliminary estimation of arriving angles. It can avoid the impact of pre-estimation bias on performance of DOA estimation and greatly reduce the amount of computation and the consumed time for DOA estimation to favor the real-time application of wideband DOA estimation. The simulation results showed that the proposed algorithm was able to provide significant performance improvement over the conventional algorithm.
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Imani far, Ehsan, Amir Akhavan, and ali asghar abniki. "Real-Time DOA Estimation of Underwater Sound Sources Using GPU." Signal and Data Processing 18, no. 2 (2021): 45–56. http://dx.doi.org/10.52547/jsdp.18.2.45.
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Hu, Ji, and Sai-wai Wong. "Design of efficient single target real-time beam tracking system." Journal of Physics: Conference Series 2245, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2245/1/012006.
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Abstract This paper builds a system for real-time tracking of a single target Radio Frequency (RF) signal source, in which the transmitter uses a low-cost HackRF One software radio transceiver platform, and the receiving antenna array uses two monopole antennas to form a 1×2 receiving array. The receiver uses Xilinx 7-Series development board Zedboard and AD9361 RF transceiver to build a wireless receiving platform to process the received signal. In this RF beam tracking system, it needs to perform Direction of Arrival (DOA) estimation on the calibrated signal, the DOA algorithm plays a crucial role in the accuracy of beam pointing and system running time, the MUSIC algorithm takes some time to conduct spectrum peak search, so the Root-Music algorithm is used here to avoid the spectrum peak search problem. To enable the Root-MUSIC algorithm to be better executed on this two-element receiving platform, this work simplifies the entire calculation process, requiring only a small amount of calculation and running time to complete the DOA estimation step, which effectively save FPGA processing time and computing resources. It provides a guarantee for the real-time performance of the system.
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Tayem, Nizar. "REAL TIME IMPLEMENATION FOR DOA ESTIMATION METHODS ON NI-PXI PLATFORM." Progress In Electromagnetics Research B 59 (2014): 103–21. http://dx.doi.org/10.2528/pierb14012307.
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Yan, Jiangzehui, Luping Xu, Chunhua Li, Weihao Tie, and Xuerong Sun. "Ultrawideband Discharge Source DOA Estimation Method Using Multiple Baseline Wideband Time-Domain Interferometry with Hilbert Transform." Mathematical Problems in Engineering 2020 (March 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/9754032.
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The research interest of ultrawideband (UWB) discharge source location estimation has increased these years. In this paper, a direction of arrival (DOA) estimation method using multiple baseline wideband time-domain interferometry with Hilbert transform for UWB discharge source is proposed based on time-domain and frequency-domain characteristics of radiated RF electromagnetic pulses (EMPs) from discharge sources. Monte Carlo simulations are then carried out; the results indicate that, the proposed method provides a better performance in UWB discharge source DOA estimation than the traditional time-domain method, especially in low signal-to-noise ratio (SNR) conditions. Moreover, the influences of antenna array configurations and incident angles of radiated EMPs on the estimation precision are also studied. It has been shown that, the accuracy of both elevation angle and azimuth angle estimation improves with the increase of the antenna element number and baseline length. As for the influence of incident angles, the estimation accuracy of elevation angle enhances when real elevation angle increases, while that of azimuth angle tends to be opposite. Meanwhile, the real azimuth angle has little effect on the DOA estimation. Finally, an experimental setup for discharge source DOA estimation is introduced and the experiment results are illustrated.
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Jančovič, Peter, Xin Zou, and Münevver Köküer. "Underdetermined DOA Estimation via Independent Component Analysis and Time-Frequency Masking." Journal of Electrical and Computer Engineering 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/450487.
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This paper presents an algorithm for the estimation of the direction of arrival (DOA) in underdetermined situations, that is, there is more sources than sensors. The algorithm performs the estimation in an iterative manner, each iteration consists of two-steps: first estimation of the DOA of a dominant source via the Independent Component Analysis and then removal of the detected source from the mixture via time-frequency masking. Experiments, performed using speech signals mixed in real environment when only two microphones are used but three and four sources are present, demonstrate that the proposed algorithm can estimate the DOAs more accurately than two previously used underdetermined DOA algorithms.
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Zhang, Jiawei, George Kossan, Richard W. Hedley, et al. "Fast 3D AML-Based Bird Song Estimation." Unmanned Systems 02, no. 03 (2014): 249–59. http://dx.doi.org/10.1142/s2301385014400044.
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In this paper, we present simulations and experimentally collected bird song data collected using a modified Voxnet acoustic array node (with four microphones) to perform 3D direction-of-arrival (DOA) estimation of various bird sources. We used the Approximate Maximum-Likelihood (AML) algorithm to construct the steering matrix in the beamforming process for the estimation of the DOA of the bird signals. While the computational burden is high in the 3D scenario, various strategies have been developed to reduce the computational burden of the algorithm for potential real-time applications. Extensive simulations and experimentally collected data are used to validate the effectiveness of the AML algorithm for 3D estimations and the usefulness of the modified Voxnet node. Both the estimated azimuths and elevations have approximately plus and minus 10 degrees of errors.
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Guo, Yuehao, Xianpeng Wang, Jinmei Shi, Lu Sun, and Xiang Lan. "Target Parameter Estimation Algorithm Based on Real-Valued HOSVD for Bistatic FDA-MIMO Radar." Remote Sensing 15, no. 5 (2023): 1192. http://dx.doi.org/10.3390/rs15051192.
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Since there is a frequency offset between each adjacent antenna of FDA radar, there exists angle-range two-dimensional dependence in the transmitter. For bistatic FDA-multiple input multiple output (MIMO) radar, range-direction of departure (DOD)-direction of arrival (DOA) information is coupled in transmitting the steering vector. How to decouple the three information has become the focus of research. Aiming at the issue of target parameter estimation of bistatic FDA-MIMO radar, a real-valued parameter estimation algorithm based on high-order-singular value decomposition (HOSVD) is developed. Firstly, for decoupling DOD and range in transmitter, it is necessary to divide the transmitter into subarrays. Then, the forward–backward averaging and unitary transformation techniques are utilized to convert complex-valued data into real-valued data. The signal subspace is obtained by HOSVD, and the two-dimensional spatial spectral function is constructed. Secondly, the dimension of spatial spectrum is reduced by the Lagrange algorithm, so that it is only related to DOA, and the DOA estimation is obtained. Then the frequency increment between subarrays is used to decouple the DOD and range information, and eliminate the phase ambiguity at the same time. Finally, the DOD and range estimation automatically matched with DOA estimation are obtained. The proposed algorithm uses the multidimensional structure of high-dimensional data to promote performance. Meanwhile, the proposed real-valued tensor-based method can effectively cut down the computing time. Simulation results verify the high efficiency of the developed method.
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Kyritsis, Alexandros, Rodoula Makri, and Nikolaos Uzunoglu. "Small UAS Online Audio DOA Estimation and Real-Time Identification Using Machine Learning." Sensors 22, no. 22 (2022): 8659. http://dx.doi.org/10.3390/s22228659.
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The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system’s efficacy for reliable UAS detection at distances exceeding 70 m.
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Mulinde, Ronald, Mayank Kaushik, Manik Attygalle, and Syed Mahfuzul Aziz. "Low-Complexity Aggregation Techniques for DOA Estimation over Wide-RF Bandwidths." Electronics 10, no. 14 (2021): 1707. http://dx.doi.org/10.3390/electronics10141707.
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Accurate direction of arrival (DOA) estimation of wideband, low-power nonstationary signals is important in many radio frequency (RF) applications. This article analyses the performance of two incoherent aggregation techniques for the DOA estimation of high chirp-rate linear frequency modulated (LFM) signals used in modern radar and electronic warfare (EW) applications. The aim is to determine suitable aggregation techniques for blind DOA estimation for real-time implementation with a frequency channelised signal. The first technique calculates a single pseudospectrum by directly combining the spatial covariance matrices from each of the frequency bins. The second technique first calculates the spatial pseudospectra from the spatial covariance matrix (SCM) from each frequency bin and then combines the spatial pseudospectra into one single estimate. Firstly, for single and multiple signal emitters, we compare the DOA estimation performance of incoherent SCM-based aggregation with that of the incoherent spatial pseudospectra-based aggregation using the root mean-squared error (RMSE). Secondly, we determine the types of signals and conditions for which these incoherent aggregation techniques are more suited. We demonstrate that the low-complexity SCM-based aggregation technique can achieve relatively good estimation performance compared to the pseudospectra-based aggregation technique for multiple narrowband signal detection. However, pseudospectra aggregation is better suited for single wideband emitter detection. Both the incoherent aggregation techniques presented in this article offer a computational advantage over the coherent processing techniques and hence are better suited for real-time implementation.
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Wen, Cai, Yan Zhou, Mingliang Tao, Jianxin Wu, and Jinye Peng. "Beam-Doppler Unitary ESPRIT for Multitarget DOA Estimation." International Journal of Antennas and Propagation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/3568286.
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High-resolution direction of arrival (DOA) estimation is a critical issue for mainbeam multitarget tracking in ground-based or airborne early warning radar system. A beam-Doppler unitary ESPRIT (BD-UESPRIT) algorithm is proposed to deal with this problem. Firstly, multiple snapshots without spatial aperture loss are obtained by using the technique of time-smoothing. Then the conjugate centrosymmetric discrete Fourier transform (DFT) matrix is used to transform the extracted data into beam-Doppler domain. Finally, the rotational invariance property of the space-time beam is exploited to estimate DOA of the target. The DOA estimation accuracy is improved greatly because the proposed algorithm takes full advantage of temporal information of the signal. Furthermore, the computational complexity of the presented algorithm is reduced dramatically, because the degree of freedom after beam transformation is very small and most of the operations are implemented in real-number domain. Numerical examples are given to verify the effectiveness of the proposed algorithm.
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Wu, Tao, Pengtao Zhang, Yiwen Li, Yangjun Gao, Chaoqi Fu, and Bo Feng. "DOA Tracking of Two-Dimensional Coherent Distribution Source Based on Fast Approximated Power Iteration." Mathematical Problems in Engineering 2020 (November 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/3219516.
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Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.
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Xu, Xiangjun, Mingwei Shen, Di Wu, and Daiyin Zhu. "An Efficient Improved OGWSBI Algorithm for Accurate Off-Grid DOA Estimation of Coherent Signals." Wireless Communications and Mobile Computing 2021 (September 15, 2021): 1–8. http://dx.doi.org/10.1155/2021/9965727.
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The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off-grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher-order off-grid model and unitary transformation for off-grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher-order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.
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Thanh, Hán Trọng, Nguyen Thanh Chuyen, and Nguyen Xuan Quyen. "DOA Estimation Method for CHAOS Radar System." Journal of Science and Technology: Issue on Information and Communications Technology 17, no. 12.2 (2019): 35. http://dx.doi.org/10.31130/ict-ud.2019.84.
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CHAOS signal has been drawing a lot of research interest recently due to its performance in security systems. In this paper, an approach to estimate the direction of target for Distributed Chaos Radar System using Total Forward - Backward Matrix Pencil (TFBMP) algorithm. This algorithm works directly on signal samples of signals received by M – element Uniform Linear Antenna array. Therefore, the correlation between the received signals does not significantly impact on its performance and efficiency. This fact permits us to estimate not only wideband incoherent signals but also wideband coherent signals. Furthermore, this algorithm can also extract the Direction Of Arrival (DOA) with only one snapshot of signal, which means that the sampling frequency in real time receivers can be considerably reduced. The simulation results for DOA of incoming CHAOS signals using the proposed approach will be shown and analyzed to verify its performance.
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Hung, Ching Jer. "Fast DOA Estimation Algorithm Based on a Combination of an Orthogonal Projection and Noise Pseudo-Eigenvector Approach." ISRN Signal Processing 2011 (December 22, 2011): 1–8. http://dx.doi.org/10.5402/2011/751670.
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This paper presents a new fast direction of arrival (DOA) estimation technique, using both the projection spectrum and the eigenspectrum. First, the rough DOA range is selected using the projection spectrum; then, a linear matrix equation is used to acquire a noise pseudo-eigenvector. Finally, the fine DOA estimation is obtained from an eigenspectrum approach based on the noise pseudo-eigenvector. Without the need to form the covariance matrix from a block of the array data and without a prior knowledge of the number of incoming signals, reduced complexity is achieved, in contrast to conventional subspace-based algorithms. Simulation results show that the proposed algorithm has a good resolution performance and deals well with both uncorrelated and correlated signals. Since the new approach can reduce computational complexity while maintaining better or similar resolution capability, it may provide wider application prospects in real-time DOA estimation when contrasted to other comparable methods.
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Wang, Peng, Jiajun Huang, Weijia He, Jingqi Zhang, and Fan Guo. "Maximum likelihood DOA estimation based on improved invasive weed optimization algorithm and application of MEMS vector hydrophone array." AIMS Mathematics 7, no. 7 (2022): 12342–63. http://dx.doi.org/10.3934/math.2022685.
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<abstract><p>Direction of arrival (DOA) estimation based on Maximum Likelihood is a common method in array signal processing, with many practical applications, but the huge amount of calculation limits the practical application. To deal with such an Maximum Likelihood (ML) DOA estimation problem, firstly, the DOA estimation model with ML for acoustic vector sensor array is developed, where the optimization standard in various cases can be unified by converting the maximum of objective function to the minimum. Secondly, based on the Invasive Weed Optimization (IWO) method which is a novel biological evolutionary algorithm, a new Improved IWO (IIWO) algorithm for DOA estimation of the acoustic vector sensor array is proposed by using ML estimation. This algorithm simulates weed invasion process for DOA estimation by adjusting the non-linear harmonic exponent of IWO algorithm adaptively. The DOA estimation accuracy has been improved, and the computation of multidimensional nonlinear optimization for the ML method has been greatly reduced in the IIWO algorithm. Finally, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) method and Tuna Swarm Optimization(TSO) algorithm, numerical simulations show that the proposed algorithm has faster convergence rate, improved accuracy in terms of Root Mean Square Error (RMSE), lower computational complexity and more robust estimation performance for ML DOA estimation. The experiment with tracking the orientation of the motorboat by Microelectronic mechanical systems (MEMS) vector hydrophone array shows the superior performance of proposed IIWO algorithm in engineering application. Therefore, the proposed ML-DOA estimation with IIWO algorithm can take into account both resolution and computation. which can meet the requirements of real-time calculation and estimation accuracy in the actual environment.</p></abstract>
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Zhao, Tianxiao, Chunbo Luo, Jianming Zhou, Dechun Guo, Ning Chen, and Pablo Casaseca-de-la-Higuera. "DoA Prediction Based Beamforming with Low Training Overhead for Highly-Mobile UAV Communication with Cellular Networks." Applied Sciences 10, no. 13 (2020): 4420. http://dx.doi.org/10.3390/app10134420.
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In supporting communications with unmanned aerial vehicles (UAVs) as aerial user equipments (aUEs) in cellular systems, the current beamforming schemes based on channel state estimation are facing severe challenges from the pilot contamination effect, especially in 5G and future networks where the cell size becomes small and the user density is high. Beamforming schemes based on signal direction of arrival (DoA) are regarded as a highly promising alternative to solve this problem. However, to achieve optimal performance for DoA-based beamforming, the error to DoA estimation during pilot signal intervals, caused by the high mobility of UAVs, must be addressed. In the meantime, the training overheads of traditional DoA estimation algorithms must be reduced to save the bandwidth for data communication. This paper investigates uplink beamforming performance enhancement based on signal DoA estimation to support UAV-cellular network communication. We propose a novel DoA estimation algorithm to predict angle variations during the intervals, which achieves high precision even when UAVs are at high mobility. The prediction process requires no pilot signals and enables timely adjustment of the steering vector when calculating the beamforming weight vector. The proposed algorithm contributes to the realisation of a beamforming scheme with real-time steering vector updates, which simultaneously maintains high beamforming gains and low training overheads. Simulation results show that, compared with the conventional DoA-based beamforming scheme, the proposed method yields more accurate DoA estimation output and higher gains. Furthermore, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second.
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Khan, Sana, Hassan Sajjad, Mehmet Ozdemir, and Ercument Arvas. "Mutual Coupling Compensation in Receiving Arrays and Its Implementation on Software Defined Radios." Applied Computational Electromagnetics Society 35, no. 11 (2021): 1433–34. http://dx.doi.org/10.47037/2020.aces.j.351185.
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Mutual coupling is compensated in a four element uniform linear receiving array using software defined radios. Direction of arrival (DoA) is estimated in real-time for the array with spacing d=lambda/4. The decoupling matrix was measured using a VNA for only one incident angle. After compensation the error in DoA estimation was reduced to 5%. Comparing the DoA results with d=lambda/2 spaced Uniform Linear Array (ULA), 1.2% error was observed. Although, the experiment was performed indoors with a low SNR, the results show a substantial improvement in the estimated DoA after compensation.
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Wu, Long, Zidan Zhang, Xu Yang, et al. "Centroid Optimization of DNN Classification in DOA Estimation for UAV." Sensors 23, no. 5 (2023): 2513. http://dx.doi.org/10.3390/s23052513.
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Classifications based on deep learning have been widely applied in the estimation of the direction of arrival (DOA) of signal. Due to the limited number of classes, the classification of DOA cannot satisfy the required prediction accuracy of signals from random azimuth in real applications. This paper presents a Centroid Optimization of deep neural network classification (CO-DNNC) to improve the estimation accuracy of DOA. CO-DNNC includes signal preprocessing, classification network, and Centroid Optimization. The DNN classification network adopts a convolutional neural network, including convolutional layers and fully connected layers. The Centroid Optimization takes the classified labels as the coordinates and calculates the azimuth of received signal according to the probabilities of the Softmax output. The experimental results show that CO-DNNC is capable of acquiring precise and accurate estimation of DOA, especially in the cases of low SNRs. In addition, CO-DNNC requires lower numbers of classes under the same condition of prediction accuracy and SNR, which reduces the complexity of the DNN network and saves training and processing time.
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Han, Guangjie, Liangtian Wan, Lei Shu, and Naixing Feng. "Two Novel DOA Estimation Approaches for Real-Time Assistant Calibration Systems in Future Vehicle Industrial." IEEE Systems Journal 11, no. 3 (2017): 1361–72. http://dx.doi.org/10.1109/jsyst.2015.2434822.
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Wang, Linwei, Bo Li, Quanrui Zhao, Xiaowei Ji, and Changjun Yu. "Online Gain-Phase Self-Calibration Method of MIMO Array Based on Statistical Characteristics of Target Angle Distribution." Wireless Communications and Mobile Computing 2022 (July 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/6951117.
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As a hot research topic, the gain-phase error self-calibration in MIMO radar systems has been investigated for many years. In this paper, we proposed a novel array error self-calibration method, termed online errors self-calibration based on feature learning (OES-FL). This method regards the statistical characteristics of the detected targets’ DOA as a prior knowledge and does not require the calibrated antenna subarray or external reference source to correct the array disturbances in real time. First, we analyse the monostatic MIMO signal model suffering gain-phase error. Then, we exploit the statistical characteristics of DOA of many targets for correcting gain-phase error of antenna array. Next, the gain-phase error estimation scheme based on LMS and the DOA deviation estimation method based on LSTM are proposed, respectively. Using real-life radar data collected at the integrated transportation hubs to generate simulation data, the proposed approach is shown to be effective in correcting gain-phase errors and, therefore, provides a promising model for online error self-calibration in monostatic MIMO radars.
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Jing, Haixia, Haiyan Wang, Zhengguo Liu, and Xiaohong Shen. "DOA Estimation for Underwater Target by Active Detection on Virtual Time Reversal Using a Uniform Linear Array." Sensors 18, no. 8 (2018): 2458. http://dx.doi.org/10.3390/s18082458.
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Aiming at addressing the problem caused by multipath effects in direction of arrival (DOA) estimation for underwater targets, a method based on the active detection on virtual time reversal (ADVTR) Capon algorithm is proposed. Unlike the conventional passive target estimation method ignoring the multipath effects but only considering the direct wave, the proposed method is closer to the actual situation in that the multipath signal propagation model is fully taken into account; in addition, active detection (AD) and virtual time reversal (VTR) processes are added, which use active detection to estimate channels, and virtual time reversal to realize focusing in a computer after the source-receive array (SRA) receives the reflected signal of the target. The combination of the two methods can greatly improve the energy of SRA and the precision of target direction estimation. With the popular acoustic field simulation tool Bellhop, the model proposed in this paper is verified. Compared with the conventional Capon method without time reversal, the simulation results show that the ADVTR Capon estimation method is far better, in terms of resolution and suppressing the sidelobes. It is suitable for the target DOA estimation under low signal-to-noise ratio (SNR) conditions. Further, we also show the ADVTR Capon estimation method works well in a real tank experiment.
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Luo, Jiawei, Yongwei Zhang, Jianyu Yang, et al. "Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar." Remote Sensing 14, no. 9 (2022): 2133. http://dx.doi.org/10.3390/rs14092133.
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The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from OK3 to OK2. Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
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Patwari, Ashish. "Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources." International Journal of Emerging Trends in Engineering Research 8, no. 5 (2020): 1972–78. http://dx.doi.org/10.30534/ijeter/2020/82852020.
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Shreedhar Bhat, Gautam, Nikhil Shankar, and Issa Panahi. "Real-time joint dereverberation and speech enhancement for hearing aid applications using edge devices." Journal of the Acoustical Society of America 150, no. 4 (2021): A348. http://dx.doi.org/10.1121/10.0008541.
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In this work, we propose a joint dereverberation and speech enhancement technique for real-time applications. Speech dereverberation is based on the coherent-to-diffuse energy ratio (CDR) measured from the direction-of-arrival (DOA) dependent complex spatial coherence function. A personalized speech enhancement (SE) technique based on joint maximum a posteriori probability (JMAP) estimation that inherently accounts for the effects of changes in acoustic path and reverberation is used. The combination of these masking functions is used to enhance the speech corrupted by reverberation and noise in a frame-by-frame process. The proposed method is implemented on the smartphone (edge device), to illustrate real-time usability. The efficiency of the proposed method is evaluated using speech quality and intelligibility measures and compared with that of other benchmark techniques.
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Jung, Yongchul, Hohyub Jeon, Seongjoo Lee, and Yunho Jung. "Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar." Electronics 10, no. 6 (2021): 695. http://dx.doi.org/10.3390/electronics10060695.
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The estimation of signal parameters via rotational invariance techniques (ESPRIT) is an algorithm that uses the shift-invariant properties of the array antenna to estimate the direction-of-arrival (DOA) of signals received in the array antenna. Since the ESPRIT algorithm requires high-complexity operations such as covariance matrix and eigenvalue decomposition, a hardware processor must be implemented such that the DOA is estimated in real time. Additionally, the ESPRIT processor should support a scalable number of antenna configuration for DOA estimation in various applications because the performance of ESPRIT depends on the number of antennas. Therefore, we propose an ESPRIT processor that supports two to eight scalable antenna configuration. In addition, since the proposed ESPRIT processor is based on multiple invariances (MI) algorithm, it can achieve a much better performance than the existing ESPRIT processor. The execution time is reduced by simplifying the Jacobi method, which has the most significant computational complexity for calculating eigenvalue decomposition (EVD) in ESPRIT. Moreover, the ESPRIT processor was designed using hardware description language (HDL), and an FPGA-based verification was performed. The proposed ESPRIT processor was implemented with 10,088 slice registers, 18,207 LUTs, and 80 DSPs, and the slice register, LUT, and DSP were reduced by up to 71.45%, 54.5%, and 68.38%, respectively, compared to the existing structure.
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Cong, Jingyu, Xianpeng Wang, Xiang Lan, Mengxing Huang, and Liangtian Wan. "Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network." Remote Sensing 13, no. 10 (2021): 1956. http://dx.doi.org/10.3390/rs13101956.
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The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.
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Wang, Juan, and Liu Lu. "Broadband Coherent Signal Direction Finding Algorithm Improvement." Applied Mechanics and Materials 743 (March 2015): 385–89. http://dx.doi.org/10.4028/www.scientific.net/amm.743.385.
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TCT for broadband coherent signal direction finding algorithm, the problem of large amount of calculation, an improved method of direction finding is studied, and the only focus signal subspace of the matrix is used to calculate, able to quickly estimate two-dimensional broadband coherent signal. Compared with the traditional TCT algorithm, in the case of without affecting the estimation precision greatly reduce amount of calculation and improve the signal DOA estimation of broadband real-time performance, and in an arbitrary planar array for the model computer simulation experiments have been carried out to verify the performance of it.
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Pandey, Saurabh K., and Mukesh A. Zaveri. "DoA-Based Event Localization Using Uniform Concentric Circular Array in the IoT Environment." Computer Journal 62, no. 10 (2019): 1403–25. http://dx.doi.org/10.1093/comjnl/bxz024.
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Abstract Response and recovery are the two most crucial aspects associated with post disaster management. Both these operations need real-time data and location at which the event occurs. These operations require event-based collection of data at critical times. The events may occur at any place and the data may be needed from either or all of the events for strategic planning and event handling. It is a challenging task to process the events whose locations are uncertain. In this view, there is a need to know the real-time location of events occurring in the surrounding. Moreover, there is a possibility of huge amount of signal processing among the devices deployed in the terrain. In this context, an event localization algorithm is proposed based on the Direction of Arrival estimation technique in the Internet of Things environment. It estimates the location of events by mapping the deployed devices using concentric circular array in the region. Further, the Cramer–Rao bound for the proposed algorithm is derived and compared with the existing schemes for efficacy. The algorithm is implemented on real test bed and presented with comparative evaluation to validate the work.
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Yan, Feng-Gang, Shuai Liu, Jun Wang, and Ming Jin. "Two-Step Root-MUSIC for Direction of Arrival Estimation without EVD/SVD Computation." International Journal of Antennas and Propagation 2018 (August 6, 2018): 1–8. http://dx.doi.org/10.1155/2018/9695326.
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Most popular techniques for super-resolution direction of arrival (DOA) estimation rely on an eigen-decomposition (EVD) or a singular value decomposition (SVD) computation to determine the signal/noise subspace, which is computationally expensive for real-time applications. A two-step root multiple signal classification (TS-root-MUSIC) algorithm is proposed to avoid the complex EVD/SVD computation using a uniform linear array (ULA) based on a mild assumption that the number of signals is less than half that of sensors. The ULA is divided into two subarrays, and three noise-free cross-correlation matrices are constructed using data collected by the two subarrays. A low-complexity linear operation is derived to obtain a rough noise subspace for a first-step DOA estimate. The performance is further enhanced in the second step by using the first-step result to renew the previous estimated noise subspace with a slightly increased complexity. The new technique can provide close root mean square error (RMSE) performance to root-MUSIC with reduced computational burden, which are verified by numerical simulations.
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Felis-Enguix, Ivan, Jorge Otero-Vega, María Campo-Valera, I. Villó-Pérez, and J. L. Gómez-Tornero. "Practical Aspects of Acoustic Leaky-Wave Antennas Applied to Underwater Direction Finding." Engineering Proceedings 2, no. 1 (2020): 93. http://dx.doi.org/10.3390/ecsa-7-08218.
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Acoustic leaky-wave antennas (ALWAs) have demonstrated the capacity to steer directive sound waves in frequency-dependent directions, due to the inherent dispersive radiation characteristic of leaky modes. Compared to more conventional uniform linear array (ULA) acoustic traducers for electronic beam steering (which rely on multiple sensors), the ALWA allows for single microphone operation. Thus, ALWAs offer a direct mechanism to scan a directive acoustic beam in the angular space by simply sweeping the operating frequency of the acoustic signal, which envisions cost-efficient single-transducer direction finders for SONAR applications. In this paper, we study for the first time some important features of an ALWA for acoustic underwater Direction-of-Arrival (DoA) estimation applications. First, we report for the first time on the necessity to shape the radiated ALWA beams in both far- and near-field zones to improve the DoA estimation performance, following similar techniques recently applied for low-cost frequency-scanned direction-finding radars based on LWAs. Furthermore, the capacity to reduce the Side Lobe Level (SLL) has been analyzed in order to improve performance, demonstrating aperture tapering techniques to the ALWA for the first time. These acoustic behaviour aspects have a considerable interest in real applications of ALWA in innovative SONAR systems for underwater scenarios.
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Guo, Qiang, and Yun Fei An. "A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF." Applied Mechanics and Materials 610 (August 2014): 339–44. http://dx.doi.org/10.4028/www.scientific.net/amm.610.339.
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A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.
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Sun, Chi-Chia, Jürgen Götze, and Gene Eu Jan. "Parallel Jacobi EVD Methods on Integrated Circuits." VLSI Design 2014 (July 20, 2014): 1–9. http://dx.doi.org/10.1155/2014/596103.
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Design strategies for parallel iterative algorithms are presented. In order to further study different tradeoff strategies in design criteria for integrated circuits, A 10 × 10 Jacobi Brent-Luk-EVD array with the simplified μ-CORDIC processor is used as an example. The experimental results show that using the μ-CORDIC processor is beneficial for the design criteria as it yields a smaller area, faster overall computation time, and less energy consumption than the regular CORDIC processor. It is worth to notice that the proposed parallel EVD method can be applied to real-time and low-power array signal processing algorithms performing beamforming or DOA estimation.
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Goyal, M., and U. Verma. "Zonal trend-agrometeorological models for wheat yield estimation in Haryana." Journal of Applied and Natural Science 8, no. 3 (2016): 1485–92. http://dx.doi.org/10.31018/jans.v8i3.988.
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An attempt has been made to assess the impact of weather variables for district-level wheat yield estimation in Haryana. Fortnightly weather data and trend based yield were used for developing the zonal trendagrometeorological (agromet) models within the framework of multiple linear regression and discriminant function analyses. The district level wheat yield forecasts, percent deviations from the real time wheat yield (s) and root mean square error(s) at zonal level show a preference of using discriminant/weather scores as regressors in almost all the considered districts of the state. Zonal trend-agromet models provided considerable improvement in district-level wheat yield prediction moreover the yield estimates may be obtained 4-5 weeks in advance of the harvest time. The estimated yield(s) from the selected zonal models showed good agreement with State Department of Agriculture (DOA) wheat yields by showing less than 5 percent deviations in 9 districts and 6-11 percent deviations in the remaining 9 districts under consideration.
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Zhang, Shizhao, and Shengchun Piao. "Broadband Sound Intensity Interference Frequency Periodicity and Pulse Source Localization." Journal of Marine Science and Engineering 9, no. 2 (2021): 200. http://dx.doi.org/10.3390/jmse9020200.
Full textAbstract:
In order to analyze the frequency periodicity characteristics of acoustic field interference and realize acoustic source ranging (ASR), the normal mode model is used to analyze the interference characteristics of the broadband acoustic field under the condition of horizontally layered medium; the broadband received signal field when the broadband pulse signal passes through the acoustic field is also simulated. The variation of interference patterns with frequency is analyzed, and their spatial interference characteristics and mechanisms are analyzed. Based on the interference theory, the relation between the acoustic source range and the frequency periodicity of the broadband acoustic intensity interference is derived. Simulation and experimental results show that this relation can accurately estimate the far-field acoustic source range, and the estimation accuracy and real-time performance are greatly improved compared with previous methods. Besides, simulation shows that the method combined with multiple-receiver ranging obtains high-precision direction of arrival (DOA) estimation as well as ASR. The relation between acoustic source position and broadband acoustic field interference frequency periodicity can be used to improve far-field ASR and DOA estimation, which is of great value for oceanography, marine engineering, and marine military. In addition, this relation can also be extended to that between the modal interference frequency periodicity and other related parameters in other physical fields for parameter inversion.
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Xiong, Zhengqiang, Tao Sun, Zhengxing Wang, Yuhao Wu, Jie Yin, and Chaoqun Duan. "A Novel Efficient Passive Spatial Orientation Detection Method of UMT Enabled by ISB." Shock and Vibration 2020 (September 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8837071.
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The passive detection and direction-of-arrival (DOA) estimation problem is of great importance in many underwater applications such as target reconnaissance and data collection. In this paper, an Efficient Correlation-based Orientation Detection (ECOD) method is proposed to achieve high efficiency. Without high computational complexity in any Transform Domain, the time consumption of ECOD is largely reduced, which is especially critical for underwater intrusion detection, territorial waters protection, and many other real-time underwater applications. To achieve good invisibility, we design an intelligent submerged buoy (ISB) structure, which consists of six embedded hydrophones and an in situ electronic control unit (IECU). As a supplement to solutions against complex underwater environments, a hybrid ECOD method is also developed by involving the cooperation from underwater distributed sensor networks. To be specific, when high SNR signals are not recorded by a single ISB node, other distributed sensors are scheduled to assist in cooperative sensing. Simulation experiments demonstrate the efficiency of the ECOD method in passive 3D spatial orientation of underwater acoustic target and show that the ECOD method has a better performance in time consumption compared with general DOA algorithms.
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Stanković, Zoran, Nebojša Dončov, Bratislav Milovanović, and Ivan Milovanović. "Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model." International Journal of Antennas and Propagation 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/542614.
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An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM) sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN) and the Multilayer Perceptron (MLP) networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA) estimation within the training boundaries which is illustrated on the appropriate example.
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Morab, Feroz, Rajeshwari Hegde, and Veena N. Hegde. "High Resolution Detection, Estimation and Location Using GTF DoA Method for Smart Antenna System." Traitement du Signal 39, no. 3 (2022): 1039–50. http://dx.doi.org/10.18280/ts.390332.
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Accommodating multiple users within the limited available bandwidth and providing the same Quality of Service (QoS) to all the users is a challenging task. Channel capacity can be increased by using the Spatial Division Multiple Access (SDMA) technique. Smart Antenna Systems are used to implement the SDMA technique in Real-Time and it also helps in finding the high-resolution Direction of Arrival (DoA) detection of the desired mobile users. In this paper novel Gaussian Triangular Factor (GTF) method is proposed for the detection of the desired mobile users from the 3-D spatial domain. This method is based on vector subspaces, which perform the triangular decomposition of the entire Eigenspace into the Lower Element Factor (LEF) and Upper Element Factor (UEF). These are then supplied for the computation of the power spectrum where peaks represent the detected locations of the desired mobile users in the 3-D spatial field. The proposed method was able to detect all the desired users, which were spaced nearer or far apart spatially, it provided high-quality detection regardless of the number of antenna elements used at the Base Station (BS). The GTF Method was able to detect all the desired users under the presence of heavy noise, fading, and interference. It was able to suppress the side lobes, back lobes, and grating lobes thus immensely improving the detection quality, detection range, system power consumption, detection efficiency, and effectiveness. The proposed GTF method was compared with several existing methods and it provided the best results for different performance parameters like Detection Error, Resolution, Time Complexity, and Disturbance Error.
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Ravita, Ravita, and Urmil Verma. "Use of crop condition based dummy regressor and weather input for parameter estimation of mustard yield forecast models." Journal of Applied and Natural Science 9, no. 3 (2017): 1703–9. http://dx.doi.org/10.31018/jans.v9i3.1425.
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Parameter estimation in statistical modelling plays a crucial role in the real world phenomena. Several alternative analyses may be required for the purpose. In this paper, standard linear regression and multivariate statistical analyses were carried out to achieve the district-level rapeseed-mustard yield estimation in Haryana State (India). The study revealed that the zonal weather models incorporating crop condition term as dummy regressor(s) had the desired predictive accuracy. The model based mustard yield(s) indicated good agreement with State Department of Agriculture (DOA) yield estimates by showing 5-10 percent deviations in most of the mustard growing districts however for two-three districts, it gave 12-13 percent deviations possibly due to the smaller set of data available for those districts. The crop yield estimates on the basis of developed models may be obtained 4-5 weeks in advance of the harvest time.
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Kim, Sangdong, Bongseok Kim, and Jonghun Lee. "Low-Complexity-Based RD-MUSIC with Extrapolation for Joint TOA and DOA at Automotive FMCW Radar Systems." Journal of Sensors 2020 (June 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/7342385.
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Low-complexity-based reduced-dimension–multiple-signal classification (RD-MUSIC) is proposed with extrapolation for joint time delay of arrivals (TOA) and direction of arrivals (DOA) at automotive frequency-modulated continuous-wave (FMCW) radar systems. When a vehicle is driving on the road, the automotive FMCW radar can estimate the position of multiple other vehicles, because it can estimate multiple parameters, such as TOA and DOA. Over time, the requirement of the accuracy and resolution parameters of automotive FMCW radar is increasing. To accurately estimate the parameters of multiple vehicles, such as range and angle, it is difficult to use a low-resolution algorithm, such as the two-dimensional fast Fourier transform. To improve parameter estimation performance, high-resolution algorithms, such as the 2D-MUSIC, are required. However, the conventional high-resolution methods have a high complexity and, thus, are not applicable to a real-time radar system for a vehicle. Therefore, in this work, a low-complexity RD-MUSIC with extrapolation algorithm is proposed to have a resolution similar to that of a high-resolution algorithm to estimate the position of other vehicles. Compared with conventional low complexity high resolution, in experimental results, the proposed method had better performance.
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CAO, JUNWEI, and ZHENGQI HE. "MULTIPLE SIGNAL CLASSIFICATION FOR GRAVITATIONAL WAVE BURST SEARCH." International Journal of Modern Physics: Conference Series 23 (January 2013): 74–81. http://dx.doi.org/10.1142/s2010194513011094.
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This work is mainly focused on the application of the multiple signal classification (MUSIC) algorithm for gravitational wave burst search. This algorithm extracts important gravitational wave characteristics from signals coming from detectors with arbitrary position, orientation and noise covariance. In this paper, the MUSIC algorithm is described in detail along with the necessary adjustments required for gravitational wave burst search. The algorithm's performance is measured using simulated signals and noise. MUSIC is compared with the Q-transform for signal triggering and with Bayesian analysis for direction of arrival (DOA) estimation, using the Ω-pipeline. Experimental results show that MUSIC has a lower resolution but is faster. MUSIC is a promising tool for real-time gravitational wave search for multi-messenger astronomy.
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Chen, Hui, Kai Chen, Kaifeng Cheng, Qinyu Chen, Yuxiang Fu, and Li Li. "An Efficient Hardware Accelerator for the MUSIC Algorithm." Electronics 8, no. 5 (2019): 511. http://dx.doi.org/10.3390/electronics8050511.
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As a classical DOA (direction of arrival) estimation algorithm, the multiple signal classification (MUSIC) algorithm can estimate the direction of signal incidence. A major bottleneck in the application of this algorithm is the large computation amount, so accelerating the algorithm to meet the requirements of high real-time and high precision is the focus. In this paper, we design an efficient and reconfigurable accelerator to implement the MUSIC algorithm. Initially, we propose a hardware-friendly MUSIC algorithm without the eigenstructure decomposition of the covariance matrix, which is time consuming and accounts for about 60% of the whole computation. Furthermore, to reduce the computation of the covariance matrix, this paper utilizes the conjugate symmetry property of it and the way of iterative storage, which can also lessen memory access time. Finally, we adopt the stepwise search method to realize the spectral peak search, which can meet the requirements of 1° and 0.1° precision. The accelerator can operate at a maximum frequency of 1 GHz with a 4,765,475.4 μm2 area, and the power dissipation is 238.27 mW after the gate-level synthesis under the TSMC 40-nm CMOS technology with the Synopsys Design Compiler. Our implementation can accelerate the algorithm to meet the high real-time and high precision requirements in applications. Assuming that the case is an eight-element uniform linear array, a single signal source, and 128 snapshots, the computation times of the algorithm in our architecture are 2.8 μs and 22.7 μs for covariance matrix estimation and spectral peak search, respectively.
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Yin, Ming, Kai Yu, and Zhi Wang. "Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9641608.
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For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.
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Xu, Wencong, Bingshu Chen, Yandong Li, Yue Hu, Jianxun Li, and Zijing Zeng. "Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength Represented by Antenna Gain Array Manifold." Sensors 22, no. 14 (2022): 5406. http://dx.doi.org/10.3390/s22145406.
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Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipment. PD direction of arrival (DOA) algorithms using conventional beam forming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, making them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then, according to the positioning requirements, the antenna array and its arrangement are developed and optimized. Simulation results suggested that the algorithm has reliable direction-finding performance in the form of six elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is 3.39°, which meets the need for partial discharge DOA estimation using inspection robots in substations.
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Hammed, Khurram, Sajjad Ahmed Ghauri, and M. Salman Qamar. "Biological Inspired Stochastic Optimization Technique (PSO) for DOA and Amplitude Estimation of Antenna Arrays Signal Processing in RADAR Communication System." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9871826.
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This paper presents a stochastic global optimization technique known as Particle Swarm Optimization (PSO) for joint estimation of amplitude and direction of arrival of the targets in RADAR communication system. The proposed scheme is an excellent optimization methodology and a promising approach for solving the DOA problems in communication systems. Moreover, PSO is quite suitable for real time scenario and easy to implement in hardware. In this study, uniform linear array is used and targets are supposed to be in far field of the arrays. Formulation of the fitness function is based on mean square error and this function requires a single snapshot to obtain the best possible solution. To check the accuracy of the algorithm, all of the results are taken by varying the number of antenna elements and targets. Finally, these results are compared with existing heuristic techniques to show the accuracy of PSO.
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Dehghan Firoozabadi, Ali, Pablo Irarrazaval, Pablo Adasme, David Zabala-Blanco, Pablo Palacios-Játiva, and Cesar Azurdia-Meza. "3D Multiple Sound Source Localization by Proposed Cuboids Nested Microphone Array in Combination with Adaptive Wavelet-Based Subband GEVD." Electronics 9, no. 5 (2020): 867. http://dx.doi.org/10.3390/electronics9050867.
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Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate the number of speakers, along with the speaker’s location, and with high accuracy is required in real-time conditions. The spatial aliasing is an undesirable effect of the use of microphone arrays, which decreases the accuracy of localization algorithms in noisy and reverberant conditions. In this article, a cuboids nested microphone array (CuNMA) is first proposed for eliminating the spatial aliasing. The CuNMA is designed to receive the speech signal of all speakers in different directions. In addition, the inter-microphone distance is adjusted for considering enough microphone pairs for each subarray, which prepares appropriate information for 3D sound source localization. Subsequently, a speech spectral estimation method is considered for evaluating the speech spectrum components. The suitable spectrum components are selected and the undesirable components are denied in the localization process. The speech information is different in frequency bands. Therefore, the adaptive wavelet transform is used for subband processing in the proposed algorithm. The generalized eigenvalue decomposition (GEVD) method is implemented in sub-bands on all nested microphone pairs, and the probability density function (PDF) is calculated for estimating the direction of arrival (DOA) in different sub-bands and continuing frames. The proper PDFs are selected by thresholding on the standard deviation (SD) of the estimated DOAs and the rest are eliminated. This process is repeated on time frames to extract the best DOAs. Finally, K-means clustering and silhouette criteria are considered for DOAs classification in order to estimate the number of clusters (speakers) and the related DOAs. All DOAs in each cluster are intersected for estimating the position of the 3D speakers. The closest point to all DOA planes is selected as a speaker position. The proposed method is compared with a hierarchical grid (HiGRID), perpendicular cross-spectra fusion (PCSF), time-frequency wise spatial spectrum clustering (TF-wise SSC), and spectral source model-deep neural network (SSM-DNN) algorithms based on the accuracy and computational complexity of real and simulated data in noisy and reverberant conditions. The results show the superiority of the proposed method in comparison with other previous works.
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Komeylian, Somayeh, and Christopher Paolini. "Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform." Sensors 23, no. 3 (2023): 1742. http://dx.doi.org/10.3390/s23031742.
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To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.
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Wildi, Marc. "Real-Time Signal Extraction: a Shift of Perspective." Studies of Applied Economics 28, no. 3 (2021): 497–518. http://dx.doi.org/10.25115/eea.v28i3.4739.
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Real-time signal extraction (RTSE) concerns the determination of optimal asymmetric filters towards the end of a time series where ⎯otherwise desirable⎯ symmetric filters cannot be applied anymore. The attractiveness of this particular estimation problem resides in the generality of its scope. For illustrative purposes we here stress realtime monitoring of the US-economy as well as multi-step ahead forecasting. Traditionally, the estimation problem addressed by RTSE is tackled in the methodological framework of the classical maximum likelihood paradigm. We here question the adequacy of this general parametric approach. In particular, we review a statistical apparatus⎯the DFA⎯ consisting of optimization criteria, diagnostics and tests which accounts for alternative user-relevant aspects of the estimation problem. Interestingly, this customization relates to an uncertainty principle which entails a fundamental shift of perspective. As a result, RTSE emerges as an autonomous discipline with proprietary concepts and statistics. With little suggestive power we may interpret the DFA as a generalization of the traditional model-based approach to more general enquiries about the future than the classical one-step ahead inference.
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Arjas, Arttu, Andreas Hauptmann, and Mikko J. Sillanpää. "Estimation of dynamic SNP-heritability with Bayesian Gaussian process models." Bioinformatics 36, no. 12 (2020): 3795–802. http://dx.doi.org/10.1093/bioinformatics/btaa199.
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Abstract Motivation Improved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. Results We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which ‘borrows strength’ from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals. Availability and implementation The C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA. Supplementary information Supplementary data are available at Bioinformatics online.