Journal articles: 'Engineering. Radar. Signal processing'

1

Haykin, S. "Radar signal processing." IEEE ASSP Magazine 2, no. 2 (1985): 2–18. http://dx.doi.org/10.1109/massp.1985.1163737.

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Ou, Jianping, Jun Zhang, and Ronghui Zhan. "Processing Technology Based on Radar Signal Design and Classification." International Journal of Aerospace Engineering 2020 (January 17, 2020): 1–19. http://dx.doi.org/10.1155/2020/4673763.

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It is well known that the application of radar is becoming more and more popular with the development of the signal technology progress. This paper lists the current radar signal research, the technical progress achieved, and the existing limitations. According to radar signal respective characteristics, the design and classification of the radar signal are introduced to reflect signal’s differences and advantages. The multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, and Doppler method. The transmission process of radar signal is summarized, including the transmission steps of radar signal, the factors affecting radar signal transmission, and radar information screening. The design method of radar signal and the corresponding signal characteristics are compared in terms of performance improvement. Radar signal classification method and related influencing factors are also contrasted and narrated. Radar signal processing technology is described in detail including multidisciplinary technology synthesis. Adaptive radar signal process, pulse compression management, and digital filtering Doppler method are very effective technical means, which has its own unique advantages. At last, the future research trends and challenges of technologies of the radar signals are proposed. The conclusions obtained are beneficial to promote the further promotion applications both in theory and practice. The study work of this paper will be useful for choosing more reasonable radar signal processing technology methods.

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Hu, Guorong, Yueqiu Han, and Leonard Chin. "ASSP-based radar signal processing." Journal of Electronics (China) 17, no. 2 (2000): 146–52. http://dx.doi.org/10.1007/bf02903192.

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Sekine, Matsuo, and Shuji Sayama. "Advances in Radar Signal Processing Techniques." IEEJ Transactions on Fundamentals and Materials 124, no. 1 (2004): 31–34. http://dx.doi.org/10.1541/ieejfms.124.31.

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Pease, Herman. "Digital signal processing in airborne radar." Digital Signal Processing 2, no. 1 (1992): 44–46. http://dx.doi.org/10.1016/1051-2004(92)90023-r.

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Wardrop, B. "Book review: Aspects of Radar Signal Processing." IEE Proceedings F Radar and Signal Processing 136, no. 1 (1989): 62. http://dx.doi.org/10.1049/ip-f-2.1989.0009.

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Lainiotis, D. G., Paraskevas Papaparaskeva, and Kostas Plataniotis. "Nonlinear filtering for LIDAR signal processing." Mathematical Problems in Engineering 2, no. 5 (1996): 367–92. http://dx.doi.org/10.1155/s1024123x96000397.

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LIDAR (Laser Integrated Radar) is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF), which has been the standard nonlinear filter in past engineering applications.

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Sytnik, Oleg. "SIGNAL PROCESSING ALGORITHM IN MULTICHANNEL RESCUE RADAR." Telecommunications and Radio Engineering 78, no. 17 (2019): 1537–47. http://dx.doi.org/10.1615/telecomradeng.v78.i17.30.

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Barbarossa, S., M. Capece, and G. Picardi. "Complex autocorrelation estimators in radar signal processing." Electronics Letters 21, no. 17 (1985): 752. http://dx.doi.org/10.1049/el:19850530.

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Kulpa, K., K. Lukin, W. Miceli, and T. Thayaparan. "Editorial: Signal processing in noise radar technology." IET Radar, Sonar & Navigation 2, no. 4 (2008): 229–32. http://dx.doi.org/10.1049/iet-rsn:20089017.

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Hongbing, Ji, Fan Laiyao, and Wang Jianjun. "Signal processing in satellite-borne radar altimeter." Journal of Electronics (China) 13, no. 3 (1996): 242–48. http://dx.doi.org/10.1007/bf02685834.

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Long, Teng, Cheng Hu, Rui Wang, et al. "Entomological Radar Overview: System and Signal Processing." IEEE Aerospace and Electronic Systems Magazine 35, no. 1 (2020): 20–32. http://dx.doi.org/10.1109/maes.2019.2955575.

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Zuo, Xuzhou, Chunguang Ma, Jianping Xiao, and Qing Zhao. "Application of Borehole Radar Data Processing Based on Empirical Mode Decomposition." Journal of Environmental and Engineering Geophysics 24, no. 3 (2019): 409–18. http://dx.doi.org/10.2113/jeeg24.3.409.

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Borehole Radar (BHR) uses ultra-wideband electromagnetic (EM) waves to image discontinuities in formations. It has been a major bottleneck to extend BHR applications to obtain a clear and high-resolution radar profile in a complex and noisy environment, which increases ambiguity in the geology interpretation. To avoid this increased ambiguity in the geology interpretation, we proposed a scheme based on the empirical mode decomposition (EMD) and complex signal analysis theory to process the BHR data with low signal to noise ratio (SNR). The scheme includes four steps. First, the original radar profile is pre-processed to avoid mode confusion and noise interference to the radar echo. Next, the EMD method is used to process a single-channel radar dataset and to analyze the frequency components of the radar signal. Various intrinsic modes of the pre-processing radar profile are also obtained by using EMD. Finally, we reconstruct the intrinsic mode profile, which contains information about the formation, calculate the complex signals of the reconstructed radar profile using the Hilbert transform, extract the three instantaneous attributes (instantaneous amplitude, instantaneous phase, and instantaneous frequency), and draw the separate instantaneous attributes profiles. This processing scheme provides both the conventional time-distance profile also in addition to the three instantaneous attributes. The additional attributes reduce ambiguity when evaluating the original radar profile and avoid the deviation relying solely on a conventional time-distance profile. An actual radar profile, which was obtained by a BHR system in a limestone fracture zone, is used to verify the effectiveness of instantaneous attributes for improving interpretation accuracy. The results demonstrate that the EMD method is superior in processing the BHR signal under a low SNR and has the capability to separate the high-low components of the radar echo effectively.

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Huuskonen, Asko, Mikko Kurri, Harri Hohti, Hans Beekhuis, Hidde Leijnse, and Iwan Holleman. "Radar Performance Monitoring Using the Angular Width of the Solar Image." Journal of Atmospheric and Oceanic Technology 31, no. 8 (2014): 1704–12. http://dx.doi.org/10.1175/jtech-d-13-00246.1.

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Abstract A method for the operational monitoring of the weather radar antenna mechanics and signal processing is presented. The method is based on the analysis of sun signals in the polar volume data produced during the operational scanning of weather radars. Depending on the hardware of the radar, the volume coverage pattern, the season, and the latitude of the radar, several tens of sun hits are found per day. The method is an extension of that for determining the weather radar antenna pointing and for monitoring the receiver stability and the differential reflectivity offset. In the method the width of the sun image in elevation and in azimuth is analyzed from the data, together with the center point position and the total power, analyzed in the earlier methods. This paper describes how the width values are obtained in the majority of cases without affecting the quality of the position and power values. Results from the daily analysis reveal signal processing features and failures that are difficult to find out otherwise in weather data. Moreover, they provide a tool for monitoring the stability of the antenna system, and hence the method has great potential for routine monitoring of radar signal processing and the antenna mechanics. Hence, it is recommended that the operational solar analysis be extended into the analysis of the width.

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Yang, Zhen, Jiming Cheng, Qingjie Qi, Xin Li, and Yuning Wang. "A Method of UWB Radar Vital Detection Based on P Time Extraction of Strong Vital Signs." Journal of Sensors 2021 (September 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/7294604.

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The vital sign information in the echo signal of the UWB radar is weak, because of the interference of complex noise. In this paper, a method named P times extraction of strong vital signs for processing echo signals of UWB radars is proposed. Different noises can be distinguished by the cumulative probability distribution of the echo signal and using different methods for processing according to corresponding characteristics. The vital sign information which most clearly represents the trapped person is selected using P times extraction of strong vital signs; then, the respiration and heartbeat rates are extracted. At 5 different distances, multiple sets of tests were carried out on static trapped persons and micromovement trapped persons and using a computer to extract vital signs from the obtained data. Experimental data shows that the algorithm proposed in this paper can extract the respiration and heartbeat rates of trapped persons, with small relative errors and variances, and has a certain reference value for UWB radar signal processing.

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Abdu, Fahad Jibrin, Yixiong Zhang, Maozhong Fu, Yuhan Li, and Zhenmiao Deng. "Application of Deep Learning on Millimeter-Wave Radar Signals: A Review." Sensors 21, no. 6 (2021): 1951. http://dx.doi.org/10.3390/s21061951.

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The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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Xing, Mengdao, Zhong Lu, and Hanwen Yu. "InSAR Signal and Data Processing." Sensors 20, no. 13 (2020): 3801. http://dx.doi.org/10.3390/s20133801.

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Lensu, Timo. "Synthetic aperture radar — systems and signal processing." Signal Processing 29, no. 1 (1992): 107. http://dx.doi.org/10.1016/0165-1684(92)90103-4.

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Nguyen, Cuong M., and V. Chandrasekar. "Gaussian Model Adaptive Processing in Time Domain (GMAP-TD) for Weather Radars." Journal of Atmospheric and Oceanic Technology 30, no. 11 (2013): 2571–84. http://dx.doi.org/10.1175/jtech-d-12-00215.1.

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Abstract The Gaussian model adaptive processing in the time domain (GMAP-TD) method for ground clutter suppression and signal spectral moment estimation for weather radars is presented. The technique transforms the clutter component of a weather radar return signal to noise. Additionally, an interpolation procedure has been developed to recover the portion of weather echoes that overlap clutter. It is shown that GMAP-TD improves the performance over the GMAP algorithm that operates in the frequency domain using both signal simulations and experimental observations. Furthermore, GMAP-TD can be directly extended for use with a staggered pulse repetition time (PRT) waveform. A detailed evaluation of GMAP-TD performance and comparison against the GMAP are done using simulated radar data and observations from the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar using uniform and staggered PRT waveform schemes.

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Thatiparthi, S. R., R. R. Gudheti, and V. Sourirajan. "MST Radar Signal Processing Using Wavelet-Based Denoising." IEEE Geoscience and Remote Sensing Letters 6, no. 4 (2009): 752–56. http://dx.doi.org/10.1109/lgrs.2009.2024556.

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Reddy, T. Sreenivasulu, and G. Ramachandra Reddy. "MST Radar Signal Processing Using Cepstral Thresholding." IEEE Transactions on Geoscience and Remote Sensing 48, no. 6 (2010): 2704–10. http://dx.doi.org/10.1109/tgrs.2009.2039937.

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22

Roe, J., S. Cussons, and A. Feltham. "Knowledge-based signal processing for radar ESM systems." IEE Proceedings F Radar and Signal Processing 137, no. 5 (1990): 293. http://dx.doi.org/10.1049/ip-f-2.1990.0045.

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23

Gottinger, Michael, Peter Gulden, and Martin Vossiek. "Coherent Signal Processing for Loosely Coupled Bistatic Radar." IEEE Transactions on Aerospace and Electronic Systems 57, no. 3 (2021): 1855–71. http://dx.doi.org/10.1109/taes.2021.3050650.

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Tang, Taiwen, Chen Wu, and Janaka Elangage. "A Signal Processing Algorithm of Two-Phase Staggered PRI and Slow Time Signal Integration for MTI Triangular FMCW Multi-Target Tracking Radars." Sensors 21, no. 7 (2021): 2296. http://dx.doi.org/10.3390/s21072296.

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In this paper, a novel signal processing algorithm for mitigating the radar blind speed problem of moving target indication (MTI) for frequency modulated continuous wave (FMCW) multi-target tracking radars is proposed. A two-phase staggered pulse repetition interval (PRI) solution is introduced to the FMCW radar system. It is implemented as a time-varying MTI filter using twice the hardware resources as compared to a uniform PRI MTI filter. The two-phase staggered PRI FMCW waveform is still periodic with a little more than twice the period of the uniform PRI radar. We also propose a slow time signal integration scheme for the radar detector using the post-fast Fourier transformation Doppler tracking loop. This scheme introduces 4.77 dB of extra signal processing gain to the signal before the radar detector compared with the original uniform PRI FMCW radar. The validation of the algorithm is done on the field programmable logic array in the loop test bed, which accurately models and emulates the target movement, line of sight propagation and radar signal processing. A simulation run of tracking 16 s of the target movement near or at the radar blind speed shows that the total degradation from the raw post-fast Fourier transformation received signal to noise ratio is about 2 dB. With a 20 dB post-processing signal to noise ratio of the proposed algorithm for the moving target at around a 20 km range and with about a −3.5 dB m2 radar cross section at a 1.5 GHz carrier frequency, the tracking errors of the two-dimensional angles with a 4×4 digital phased array are less than 0.2 degree. The range tracking error is about 28 m.

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He, Ji Yong, and Xiang Guang Chen. "Design of PowerPC-Based Radar Universal Signal Processing Unit." Advanced Materials Research 760-762 (September 2013): 1360–63. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1360.

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With the rapid development of radar system, it has put forward higher requirements in the ability of real-time signal processing, the capability of data processing and the versatility of signal processing platforms. The signal processing unit based on PowerPC processor MPC8640D can complete the calculation of complex data using the superior performance of the processor. The combination of embedded operating system VxWorks can meet the real-time requirement of the radar signal processing perfectly. Universal IO interface definition of PowerPC processors make the designed signal processing unit own excellent versatility. The use of muti-beam digital synthesis technique and the vector library in software development improves the signal processing further more.

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Bharadwaj, Nitin, V. Chandrasekar, and Francesc Junyent. "Signal Processing System for the CASA Integrated Project I Radars." Journal of Atmospheric and Oceanic Technology 27, no. 9 (2010): 1440–60. http://dx.doi.org/10.1175/2010jtecha1415.1.

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Abstract This paper describes the waveform design space and signal processing system for dual-polarization Doppler weather radar operating at X band. The performance of the waveforms is presented with ground clutter suppression capability and mitigation of range–velocity ambiguity. The operational waveform is designed based on operational requirements and system/hardware requirements. A dual–Pulse Repetition Frequency (PRF) waveform was developed and implemented for the first generation X-band radars deployed by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This paper presents an evaluation of the performance of the waveforms based on simulations and data collected by the first-generation CASA radars during operations.

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Sekine, Matsuo. "Radar Signal Processing in Instrumentation and Measurement Technology." IEEJ Transactions on Fundamentals and Materials 126, no. 6 (2006): 399–402. http://dx.doi.org/10.1541/ieejfms.126.399.

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Sytnik, O. V., S. A. Masalov, and G. P. Pochanin. "HOMOMORPHIC SIGNAL PROCESSING ALGORITHM OF GROUND PENETRATION RADAR." Telecommunications and Radio Engineering 75, no. 5 (2016): 413–23. http://dx.doi.org/10.1615/telecomradeng.v75.i5.30.

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Dong, Yunhan. "Frequency diverse array radar signal and data processing." IET Radar, Sonar & Navigation 12, no. 9 (2018): 954–63. http://dx.doi.org/10.1049/iet-rsn.2018.0031.

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Chandrasekar, V., G. R. Gray, and I. J. Caylor. "Auxiliary Signal Processing System for a Multiparameter Radar." Journal of Atmospheric and Oceanic Technology 10, no. 3 (1993): 428–31. http://dx.doi.org/10.1175/1520-0426(1993)010<0428:aspsfa>2.0.co;2.

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31

Brookner, E. "Radar signal processing and adaptive systems [Book Review]." IEEE Aerospace and Electronic Systems Magazine 15, no. 9 (2000): 47. http://dx.doi.org/10.1109/maes.2000.873478.

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Lyzenga, David R. "Polar Fourier Transform Processing of Marine Radar Signals." Journal of Atmospheric and Oceanic Technology 34, no. 2 (2017): 347–54. http://dx.doi.org/10.1175/jtech-d-16-0158.1.

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AbstractThis paper describes a method of processing marine radar signals for the purpose of generating phase-resolved surface elevation maps as well as statistical measures of ocean surface wave fields. The method is well suited to the processing of data collected by marine radars because it allows for the incorporation of effects dependent on the radar look direction relative to the propagation direction of ocean waves. Applications to Doppler radar and backscattered power measurements are described, and example results are presented using simulated radar data.

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Uma Maheswara Rao, D., T. Sreenivasulu Reddy, and G. Ramachandra Reddy. "Atmospheric radar signal processing using principal component analysis." Digital Signal Processing 32 (September 2014): 79–84. http://dx.doi.org/10.1016/j.dsp.2014.05.009.

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Quan, Nguyen Van. "A Passive Radar System for Monitoring of Coastal Areas Ship Traffic Using Satellite Illumination Signals." Journal of the Russian Universities. Radioelectronics 23, no. 3 (2020): 41–52. http://dx.doi.org/10.32603/1993-8985-2020-23-3-41-52.

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Introduction. Increasing requirements for improving of information systems for ensuring navigation safety in coastal areas of marine waters determine the search of new engineering and scientific solutions. The creation of a passive coherent location systems (PCL), based on existing sources of electromagnetic radiation (in particular, global navigation satellite system (GNSS) signals) as radar illumination of the monitored space is of particular interest. During development and implementation of the systems, there are a number of problems related to the search of highly efficient processing algorithms, to the optimization of structure and functioning modes when the system is a part of a complex multi-position monitoring system in coastal areas. Aim. Rationale of the structure of bistatic PCL system with GNSS illumination signal, analysis of methods for increasing of the level of reflected signals, development of a general signal processing algorithm of the system receiver unit, formation of proposals for the creation of multi-position radar system (MP radar) for coastal areas navigation monitoring. Materials and methods. Mathematical modeling, theory of signals, methods of digital signal processing. Results. The structure of the bistatic PCL with GNSS illumination signal for monitoring in coastal areas of marine waters to ensure navigation safety has been developed. Methods for increasing the power level of satellite signals at the input of the receiving device have been proposed. General signal processing algorithm and the algorithm of CAF calculation in the bistatic PCL system using GPS C/A code satellite signal for sea surface coastal areas monitoring have been developed. Conclusion. The considered bistatic PCL system with GNSS illumination may be applied as a part of MP radar for monitoring in areas of heavy vessel traffic to ensure the safety, for operational control of marine operations in the high seas, for quick analysis of the situation at sea in an emergency.

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Wang, HuiJuan, ZiYue Tang, YuanQing Zhao, YiChang Chen, ZhenBo Zhu, and YuanPeng Zhang. "Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination." Sensors 19, no. 24 (2019): 5341. http://dx.doi.org/10.3390/s19245341.

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A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.

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Gishkori, Shahzad, and Bernard Mulgrew. "Graph Signal Processing-Based Imaging for Synthetic Aperture Radar." IEEE Geoscience and Remote Sensing Letters 17, no. 2 (2020): 232–36. http://dx.doi.org/10.1109/lgrs.2019.2919147.

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Jiang Ge, 江舸, 杨陈 Yang Chen, 周晓青 Zhou Xiaoqing, et al. "Application of radar signal processing in terahertz imaging." High Power Laser and Particle Beams 25, no. 6 (2013): 1555–60. http://dx.doi.org/10.3788/hplpb20132506.1555.

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Bhattacharya, S. "Radar Signal Processing and its Applications [Book Review]." IEEE Circuits and Devices Magazine 22, no. 4 (2006): 37–38. http://dx.doi.org/10.1109/mcd.2006.1708391.

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Woodman, Ronald F. "Coherent radar imaging: Signal processing and statistical properties." Radio Science 32, no. 6 (1997): 2373–91. http://dx.doi.org/10.1029/97rs02017.

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Zeng, Jian Kui, and Zi Ming Dong. "A Simulation System for MIMO Radar." Advanced Materials Research 121-122 (June 2010): 633–39. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.633.

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MIMO radar (Multiple input multiple output radar) is a novel radar technique developed recently. It can achieve better detection performance than conventional phased radar. In this paper, the MIMO radar signal model is studied, and then the signal processing flow of the MIMO radar is researched. At last, a simulation platform with the Matlab is established to testify the advantage of MIMO radar over its conventional counterpart.

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Sun, Meng, Jingjing Pan, Cedric Le Bastard, Yide Wang, and Jianzhong Li. "Advanced Signal Processing Methods for Ground-Penetrating Radar: Applications to Civil Engineering." IEEE Signal Processing Magazine 36, no. 4 (2019): 74–84. http://dx.doi.org/10.1109/msp.2019.2900454.

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Torres, Sebastián M., and Christopher D. Curtis. "The Impact of Signal Processing on the Range-Weighting Function for Weather Radars." Journal of Atmospheric and Oceanic Technology 29, no. 6 (2012): 796–806. http://dx.doi.org/10.1175/jtech-d-11-00135.1.

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Abstract The range-weighting function (RWF) determines how individual scatterer contributions are weighted as a function of range to produce the meteorological data associated with a single resolution volume. The RWF is commonly defined in terms of the transmitter pulse envelope and the receiver filter impulse response, and it determines the radar range resolution. However, the effective RWF also depends on the range-time processing involved in producing estimates of meteorological variables. This is a third contributor to the RWF that has become more significant in recent years as advanced range-time processing techniques have become feasible for real-time implementation on modern radar systems. In this work, a new formulation of the RWF for weather radars that incorporates the impact of signal processing is proposed. Following the derivation based on a general signal processing model, typical scenarios are used to illustrate the variety of RWFs that can result from different range-time signal processing techniques. Finally, the RWF is used to measure range resolution and the range correlation of meteorological data.

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Szczepankiewicz, Karolina, Mateusz Malanowski, and Michał Szczepankiewicz. "Passive Radar Parallel Processing Using General-Purpose Computing on Graphics Processing Units." International Journal of Electronics and Telecommunications 61, no. 4 (2015): 357–63. http://dx.doi.org/10.1515/eletel-2015-0047.

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Abstract In the paper an implementation of signal processing chain for a passive radar is presented. The passive radar which was developed at the Warsaw University of Technology, uses FM radio and DVB-T television transmitters as ”illuminators of opportunity”. As the computational load associated with passive radar processing is very high, NVIDIA CUDA technology has been employed for effective implementation using parallel processing. The paper contains the description of the algorithms implementation and the performance results analysis.

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Mead, James B. "Comparison of Meteorological Radar Signal Detectability with Noncoherent and Spectral-Based Processing." Journal of Atmospheric and Oceanic Technology 33, no. 4 (2016): 723–39. http://dx.doi.org/10.1175/jtech-d-14-00198.1.

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AbstractDetection of meteorological radar signals is often carried out using power averaging with noise subtraction either in the time domain or the spectral domain. This paper considers the relative signal processing gain of these two methods, showing a clear advantage for spectral-domain processing when normalized spectral width is less than ~0.1. A simple expression for the optimal discrete Fourier transform (DFT) length to maximize signal processing gain is presented that depends only on the normalized spectral width and the time-domain weighting function. The relative signal processing gain between noncoherent power averaging and spectral processing is found to depend on a variety of parameters, including the radar wavelength, spectral width, available observation time, and the false alarm rate. Expressions presented for the probability of detection for noncoherent and spectral-based processing also depend on these same parameters. Results of this analysis show that DFT-based processing can provide a substantial advantage in signal processing gain and probability of detection, especially when the normalized spectral width is small and when a large number of samples are available. Noncoherent power estimation can provide superior probability of detection when the normalized spectral width is greater than ~0.1, especially when the desired false alarm rate exceeds 10%.

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Xu, F., and Y. Q. Jin. "Raw signal processing of stripmap bistatic synthetic aperture radar." IET Radar, Sonar & Navigation 3, no. 3 (2009): 233. http://dx.doi.org/10.1049/iet-rsn:20070079.

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Wang, Tingjing, Ying Zhang, Hua Zhao, and Yanxin Zhang. "Multiband Radar Signal Coherent Processing Algorithm for Motion Target." International Journal of Antennas and Propagation 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4060789.

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In real application, most aerial targets are movable. In this paper, an effective multiple subbands coherent processing method is proposed for moving target. Firstly, an echoed signal model of motion target based on geometrical theory of diffraction is established and the influence of velocity on range profile of the target is analyzed. Secondly, a method based on minimum entropy principle is used to compensate velocity. Then, incoherent factors including a quadratic phase term, a linear phase factor, a fixed factor, and an amplitude difference term are analyzed. Subsequently, efficient methods are applied to estimate other incoherent factors, except that the quadratic term is small enough to be ignored. Finally, the feasibility and performance of the proposed method are investigated through numerical simulation.

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De Martin, Lilian, Wim Van Rossum, Diogo Ribeiro, and Laura Anitori. "Sidelobe Mitigation in Noise Radar Using Sparse Signal Processing." IEEE Aerospace and Electronic Systems Magazine 35, no. 9 (2020): 32–40. http://dx.doi.org/10.1109/maes.2020.2988331.

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48

Eskelinen, P. "Principles of radar and sonar signal processing [Book Review]." IEEE Aerospace and Electronic Systems Magazine 18, no. 3 (2003): 31–32. http://dx.doi.org/10.1109/maes.2003.1193716.

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Hu, Yinan, Faruk Uysal, and Ivan Selesnick. "Wind Turbine Clutter Mitigation via Nonconvex Regularizers and Multidimensional Processing." Journal of Atmospheric and Oceanic Technology 36, no. 6 (2019): 1093–104. http://dx.doi.org/10.1175/jtech-d-18-0164.1.

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AbstractThis paper generalizes a previous formulation of signal separation problem for dynamic wind turbine clutter mitigation at weather radar systems. In this modified formulation, we use nonconvex regularizers together with multichannel overlapping group shrinkage (MOGS) to penalize weather signals and adopt multidimensional processing. We show the restored weather signals in plan position indicator (PPI) format and, to demonstrate the improvement, compare them with the ones produced by the previous method in reflectivity, spectral width, and Doppler velocity estimates of weather data. The improvement results from a better characterization of the sparsities of the weather radar returns. During the course of experiments, we observe that the proposed method successfully mitigates the wind turbine clutter and dramatically increases the signal-to-clutter ratio, even for different weather and wind turbine signatures. In addition, when the wind turbine clutter is weak in the mixture, our algorithm manages to attenuate the ground clutters and produces clutter-free weather signals favorable for further processing.

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Luong, David, and Bhashyam Balaji. "Quantum two‐mode squeezing radar and noise radar: covariance matrices for signal processing." IET Radar, Sonar & Navigation 14, no. 1 (2020): 97–104. http://dx.doi.org/10.1049/iet-rsn.2019.0090.

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Journal articles on the topic 'Engineering. Radar. Signal processing'

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