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Relevant bibliographies by topics / Target decomposition theorem

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Academic literature on the topic 'Target decomposition theorem'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Target decomposition theorem"

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Wu, Peng, Jun Wang, and Wenguang Wang. "A Novel Method of Small Target Detection in Sea Clutter." ISRN Signal Processing 2011 (June 28, 2011): 1–10. http://dx.doi.org/10.5402/2011/651790.

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Detecting low observable targets within sea clutter at low grazing angle is one of the research hotspots in radar signal processing community. In this paper, we have proposed a novel method based on polarimetric decomposition theorem. The polar characteristics of sea clutter has been analyzed, with the parameters after the decomposition of target scattering matrix. The scattering entropy and the scattering angle are the key parameters to discriminate the target from the sea clutter. The technique is designed to suppress unwanted sea clutter at polarimetric domain. Datasets from real marine radar are used to illustrate the performance of the new approach.

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Jarner, Søren F., and Wai Kong Yuen. "Conductance bounds on the L 2 convergence rate of Metropolis algorithms on unbounded state spaces." Advances in Applied Probability 36, no. 01 (March 2004): 243–66. http://dx.doi.org/10.1017/s0001867800012957.

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In this paper we derive bounds on the conductance and hence on the spectral gap of a Metropolis algorithm with a monotone, log-concave target density on an interval of ℝ. We show that the minimal conductance set has measure ½ and we use this characterization to bound the conductance in terms of the conductance of the algorithm restricted to a smaller domain. Whereas previous work on conductance has resulted in good bounds for Markov chains on bounded domains, this is the first conductance bound applicable to unbounded domains. We then show how this result can be combined with the state-decomposition theorem of Madras and Randall (2002) to bound the spectral gap of Metropolis algorithms with target distributions with monotone, log-concave tails on ℝ.

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Jarner, Søren F., and Wai Kong Yuen. "Conductance bounds on the L2 convergence rate of Metropolis algorithms on unbounded state spaces." Advances in Applied Probability 36, no. 1 (March 2004): 243–66. http://dx.doi.org/10.1239/aap/1077134472.

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In this paper we derive bounds on the conductance and hence on the spectral gap of a Metropolis algorithm with a monotone, log-concave target density on an interval of ℝ. We show that the minimal conductance set has measure ½ and we use this characterization to bound the conductance in terms of the conductance of the algorithm restricted to a smaller domain. Whereas previous work on conductance has resulted in good bounds for Markov chains on bounded domains, this is the first conductance bound applicable to unbounded domains. We then show how this result can be combined with the state-decomposition theorem of Madras and Randall (2002) to bound the spectral gap of Metropolis algorithms with target distributions with monotone, log-concave tails on ℝ.

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Hao, Yu, Nan Zou, and Guolong Liang. "Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar." Journal of Marine Science and Engineering 7, no. 3 (March 26, 2019): 80. http://dx.doi.org/10.3390/jmse7030080.

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Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

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Cloude, S. R. "Target decomposition theorems in radar scattering." Electronics Letters 21, no. 1 (January 3, 1985): 22–24. http://dx.doi.org/10.1049/el:19850018.

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Cloude, S. R., and E. Pottier. "A review of target decomposition theorems in radar polarimetry." IEEE Transactions on Geoscience and Remote Sensing 34, no. 2 (March 1996): 498–518. http://dx.doi.org/10.1109/36.485127.

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Wang, Yi, Xueyi Shang, Zewei Wang, and Rui Gao. "High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion." Applied Sciences 10, no. 20 (October 15, 2020): 7205. http://dx.doi.org/10.3390/app10207205.

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High-accuracy determination of a microseismic (MS) location is the core task in MS monitoring. In this study, a 3D multi-scale grid Green’s function database, depending on recording wavefield frequency band for the target mining area, is pre-generated based on the reciprocity theorem and 3D spectral element method (SEM). Then, a multi-scale global grid search strategy is performed based on this pre-stored Green’s function database, which can be effectively and hierarchically processed by searching for the spatial location. Numerical wavefield modeling by SEM effectively overcomes difficulties in traditional and simplified ray tracing modeling, such as difficult wavefield amplitude and multi-path modeling in 3D focusing and defusing velocity regions. In addition, as a key step for broadband waveform simulation, the source-time function estimated from a new data-driven singular value decomposition averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) was proposed to generate high-precision synthetic waveforms for better fitting observed broadband waveform than those by simple and traditional source-time function. Combining these sophisticated processing procedures, a new robust grid search and waveform inversion-based location (GSWI location) approach is integrated. In the synthetic test, we discuss and demonstrate the importance of 3D velocity model accuracy to waveform inversion-based location results for a practical MS monitoring configuration. Furthermore, the average location error of the 3D GSWI location for eight real blasting events is only 15.0 m, which is smaller than error from 3D ray tracing-based location (26.2 m) under the same velocity model. These synthetic and field application investigations prove the crucial role of 3D velocity model, finite-frequency travel-time sensitivity kernel characteristics and accurate numerical 3D broadband wavefield modeling for successful MS location in a strong heterogeneous velocity model that are induced by the presence of ore body, host rocks, complex tunnels, and large excavations.

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Trudel, M., R. Magagi, and H. B. Granberg. "Application of Target Decomposition Theorems Over Snow-Covered Forested Areas." IEEE Transactions on Geoscience and Remote Sensing 47, no. 2 (February 2009): 508–12. http://dx.doi.org/10.1109/tgrs.2008.2009122.

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Unal, C. M. H., and L. P. Ligthart. "Decomposition Theorems Applied to Random and Stationary Radar Targets." Progress In Electromagnetics Research 18 (1998): 45–66. http://dx.doi.org/10.2528/pier97040700.

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Lopez-Martinez, Carlos, Alberto Alonso-Gonzalez, and Xavier Fabregas. "Perturbation Analysis of Eigenvector-Based Target Decomposition Theorems in Radar Polarimetry." IEEE Transactions on Geoscience and Remote Sensing 52, no. 4 (April 2014): 2081–95. http://dx.doi.org/10.1109/tgrs.2013.2257802.

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Dissertations / Theses on the topic "Target decomposition theorem"

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Yong, Siow Yin. "Radar polarimetry." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FYong.pdf.

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Book chapters on the topic "Target decomposition theorem"

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Huynen, J. Richard. "Comments on Target Decomposition Theorems." In Direct and Inverse Methods in Radar Polarimetry, 387–99. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9243-2_13.

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Cloude, S. R. "Uniqueness of Target Decomposition Theorems in Radar Polarimetry." In Direct and Inverse Methods in Radar Polarimetry, 267–96. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9243-2_10.

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Conference papers on the topic "Target decomposition theorem"

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van Zyl, Jakob J. "Application of Cloude's target decomposition theorem to polarimetric imaging radar data." In San Diego '92, edited by Harold Mott and Wolfgang-Martin Boerner. SPIE, 1993. http://dx.doi.org/10.1117/12.140615.

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Tao, Liu, Huang Gao-ming, Wang Xue-song, and Xiao Shun-ping. "Statistics of SDH target decomposition theorems in radar polarimetry." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420843.

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Praks, J., J. Koskinen, A. N. Arslan, N. Kruopis, H. Alasalmi, and M. Hallikainen. "Cloude's target decomposition theorems applied to EMISAR data acquired during EMAC'95 snow campaign." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.691578.

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Lischi, Stefano, Alberto Lupidi, Elisa Giusti, and Marco Martorella. "Enhanced ATR by jointly using Coherent and Incoherent Target Decomposition theorems on polarimetric ISAR images." In 2014 European Radar Conference (EuRAD). IEEE, 2014. http://dx.doi.org/10.1109/eurad.2014.6991214.

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Liu, Zhiliang, Yaqiang Jin, and Ming J. Zuo. "Time-Frequency Representation Based on Robust Local Mean Decomposition." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65184.

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Fourier transform based frequency representation makes an underlying assumption of stationarity and linearity for the target signal whose spectrum is to be computed, and thus it is unable to track time varying characteristics of non-stationary signals that also widely exist in the physical world. Time-frequency representation (TFR) is a technique to reveal useful information included in the signals, and thus the TFR methods are very attractive to the scientific and engineering world. Local mean decomposition (LMD) is a TFR technique used in many fields, e.g. machinery fault diagnosis. Similar to Hilbert-Huang transform, it is an alternative approach to demodulate amplitude-modulation (AM) and frequency-modulation (FM) signals into a set of components, each of which is the product of an instantaneous envelope signal and a pure FM signal. TFR can then be derived by the instantaneous envelope signal and the pure FM signal. However, LMD based TFR technique still has two limitations, i.e. the end effect and the mode mixing problems. Solutions for the two limitations greatly depend on three critical parameters of LMD that are boundary condition, envelope estimation, and sifting stopping criterion. Most reported studies aiming to improve performance of LMD have focused on only one parameter a time, and thus they ignore the fact that the three parameters are not independent to each other, and all of them are needed to address the end effect and the mode mixing problems in LMD. In this paper, a robust optimization approach is proposed to improve performance of LMD through an integrated framework of parameter selection in terms of boundary condition, envelope estimation, and sifting stopping criterion. The proposed optimization approach includes three components. First, the mirror extending method is employed to deal with the boundary condition problem. Second, moving average is used as the smooth algorithm for envelope estimation of local mean and local magnitude in LMD. The fixed subset size is the only parameter that usually needs to be predefined with a prior knowledge. In this step, a self-adaptive method based on the statistics theory is proposed to automatically determine a fixed subset size of moving average for accurate envelope estimation. Third, based on the first and the second steps, a soft sifting stopping criterion is proposed to enable LMD to achieve a self-adaptive stop for each sifting process. In this last step, we define an objective function that considers both global and local characteristics of a target signal. Based on the objective function, a heuristic mechanism is proposed to automatically determine the optimal number of sifting iterations in the sifting process. Finally, numerical simulation results show the effectiveness of the robust LMD in terms of mining time-frequency representation information.

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Merrell, David, Aws Albarghouthi, and Loris D'Antoni. "Weighted Model Integration with Orthogonal Transformations." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/643.

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Weighted model counting and integration (WMC/WMI) are natural problems to which we can reduce many probabilistic inference tasks, e.g., in Bayesian networks, Markov networks, and probabilistic programs. Typically, we are given a first-order formula, where each satisfying assignment is associated with a weight---e.g., a probability of occurrence---and our goal is to compute the total weight of the formula. In this paper, we target exact inference techniques for WMI that leverage the power of satisfiability modulo theories (SMT) solvers to decompose a first-order formula in linear real arithmetic into a set of hyperrectangular regions whose weight is easy to compute. We demonstrate the challenges of hyperrectangular decomposition and present a novel technique that utilizes orthogonal transformations to transform formulas in order to enable efficient inference. Our evaluation demonstrates our technique's ability to improve the time required to achieve exact probability bounds.

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Cheng, Jing, Li Chen, Jianxun Liang, and Wei Ma. "Partitioned Adaptive Control Based on Neural Network of a Flexible Space Robot After Capture Operation." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9167.

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The partitioned adaptive control and vibration suppression of free-floating space robot with flexible arms in post-impact process are studied. At first, the dynamic model of combination system after flexible space robot system capturing a target system is established based on the collision theory; the impact effect of space robot combination system after capture operation is analyzed at the same time. Secondly, based on the double time scale decomposition theory, the unstable combination system is decomposed into fast system and slow system, representing the rigid motion of the system and the flexible vibration respectively. To satisfy the compute capacity of space-borne computer and modular design concept, the slow system is considered as a set of interconnected subsystems and a decentralized adaptive neural network control scheme is designed. Neural network is applied to approximating the unknown dynamic of the subsystems; an adaptive sliding mode controller is designed to eliminate both interconnection term and approximation error. The control algorithm has a cutting edge in independent control signal and reduced calculation amount. The Linear Quadratic Optimal control scheme is designed for fast system to suppress the elastic vibration of the flexible manipulators. At last, numerical example demonstrates the validity of the proposed composite control scheme.

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Lopez, Israel, and Nesrin Sarigul-Klijn. "Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty." In ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2009. http://dx.doi.org/10.1115/smasis2009-1315.

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Aircraft navigation can be safely accomplished by properly addressing the following: decision-making, obstacle perception, aircraft state estimation, and aircraft control. To develop a monolithic navigational system is probably an impossible task; instead a hierarchical decomposition is presented, which breaks down the safe recovery and landing of distressed aircraft into sub-problems that maximize the probability that the overall objective is achieved. Navigational performance is often hinder by in-flight damage or failures, which often results in mission failure and an inability to guide the aircraft to a safe landing. Uncertainty is a very important concern in recovery of damaged aircraft since it can cause infeasibilities, false diagnosis and prognosis causing further performance degradation and mission failure. The damaged aircraft is simulated via a simplified kinematic model. The different sources and perspectives of uncertainties in the damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning and landing is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft given uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.

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