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distributed target detection Exploiting Persymmetry in Gaussian Clutter

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第4期67卷 1022-1033页

作者： Liu, Jun Liu, Weijian Tang, Bo Zheng, Jibin Xu, Shuwen Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Anhui Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Hubei Peoples R China Natl Univ Def Technol Coll Elect Engn Changsha 410073 Hunan Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this paper, we consider the distributed target detection problem in Gaussian clutter with unknown covariance matrix. By exploiting the persymmetry of the covariance matrix, an adaptive detector is proposed according to the two-step design method. The probabilities of detection and false alarm of the proposed detector are derived in closed form, which are verified through Monte Carlo simulations. The expression for the probability of false alarm reveals that the proposed detector bears constant false alarm rate against the covariance matrix. Numerical examples illustrate that the proposed detector outperforms its counterparts, especially in the limited training data environment.

关键词： Adaptive detection persymmetry constant false alarm rate distributed target detection

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distributed target detection With Partial Observation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第6期66卷 1551-1565页

作者： Xiao, Le Liu, Yimin Huang, Tianyao Liu, Xiang Wang, Xiqin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

This paper considers the detection of a distributed target in a partial observation scenario. A distributed target model is usually adopted when the target size is larger than the range bin, for example, in some high-resolution radars. Based on the distributed target model, joint processing of several consecutive range bins can be adopted to achieve performance improvement with respect to processing just one range bin. In applications in complex electromagnetic environments, the radars may miss some of their observations, a phenomenon that usually caused by interference, spectrum sharing, and so on. This partial observation problem leads to degradation of the estimation accuracy for the disturbance (clutter plus noise) covariance matrix and target amplitude vector, which results in decline of the target detection performance. In this paper, a scheme is proposed by using the low-rank priori knowledge of clutter covariance matrix to estimate the detector's unknown parameters. Specifically, the target amplitude vector is obtained by maximizing the likelihood function, and the disturbance covariance matrix is reconstructed by solving an optimization that considers both the likelihood maximization and low-rank property of the clutter covariance matrix. The simulation results indicate that this algorithm improves the estimation accuracy, and achieves a better detection performance in the partial observation scenarios.

关键词： distributed target detection partial observation likelihood ratio test matrix completion

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distributed target detection Based on the Volume Cross-Correlation Function

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IEEE SIGNAL PROCESSING LETTERS 2018年第12期25卷 1785-1789页

作者： Xiao, Le Li, Hongbin Liu, Yimin Wang, Xiqin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

This letter addresses the detection of a subspace distributed target signal obscured by disturbance. The disturbance consists of a clutter component with an unknown subspace structure and a white noise component with unknown noise power. A detection strategy is proposed based on the volume cross-correlation function, which provides a metric that measures the linear (in) dependency between two subspaces. Simulation results indicate that the proposed detector can achieve better performance than several peer methods, without resorting to secondary data and a priori knowledge about the clutter subspace including its rank.

关键词： distributed target detection subspace target volume cross-correlation (VCC) function

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Bayesian generalised likelihood ratio tests for distributed target detection in interference and noise

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IET RADAR SONAR AND NAVIGATION 2017年第5期11卷 752-758页

作者： Gao, Yongchan Liao, Guisheng Liu, Weijian Zuo, Lei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Wuhan Radar Acad Wuhan 430019 Peoples R China

This study addresses the problem of detecting a distributed target in interference and noise with low sample support. The target signal and interference lie in two linearly independent subspaces and the spectral property of the noise is unknown. The number of available training data is too small to form a non-singular sample covariance matrix. To overcome the difficulty, the authors resort to the Bayesian framework and design two generalised likelihood ratio tests. Both detectors can reduce training requirement by utilising the a prior knowledge of the noise covariance matrix. Numerical results show that the proposed detectors provide better detection performance than the conventional solutions in training-limited scenarios.

关键词： object detection interference (signal) Bayes methods radar detection covariance matrices Bayesian generalised likelihood ratio tests distributed target detection linearly independent subspaces spectral property nonsingular sample covariance matrix noise covariance matrix signal interference

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Multi-sample clustering decision fusion for distributed target detection in wireless sensor networks

Multi-sample clustering decision fusion for distributed targ...

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第六届中国传感器网络学术会议(CWSN 2012)

作者： CHEN Lei HUANG Hai-ping WANG Ru-chuan QIN Xiao-lin WANG Qian-yi College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjin College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China

In this paper,we revisit the problem of target detection in wireless sensor networks (WSNs).Because that the practical environment of WSNs is very complex,and usually the target signal is mixed with a lot of random noises,the phenomenon impacts the performance of target detection *** paper set forth a new way to improve the system *** introduce gather multiple sample when we get the observation *** obtaining the samples,we calculate the judgments of each sensor by fusion *** last local sensors transfer their judgments to the fusion *** prove that the method is better than single sample through Monte Carlo experiments because of the full use of the observation signals.

关键词： wireless sensor network distributed target detection Neyman-Pearson criterion 0-1 decision fusion

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detection of a distributed target with direction uncertainty

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IET RADAR SONAR AND NAVIGATION 2014年第9期8卷 1177-1183页

作者： Liu, Weijian Xie, Wenchong Liu, Jun Zou, Dujian Wang, Honglin Wang, Yongliang Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Wuhan Radar Acad Key Res Lab Wuhan 430019 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China

In this study, the authors deal with the problem of detecting a distributed target in coloured noise with unknown covariance matrix. The target echoes are all assumed to come from the same direction. However, the exact direction is unknown a priori. Precisely, the signal steering vector lies in a certain known subspace, but with unknown coordinate. They derive the Wald test and two-step Wald test and also show that there is no meaningful Rao test or its two-step variation. Moreover, it is found that the two-step Wald test is equivalent to an existing direction detector. Additionally, it is shown that the Wald test possesses the constant false alarm rate property. Some numerical examples are given to compare the detection performance of the novel direction detector with its counterparts, both in the cases of matched and mismatched signals.

关键词： object detection radar detection covariance matrices vectors distributed target detection direction uncertainty coloured noise covariance matrix signal steering vector two-step Wald test Rao test constant false alarm rate property signal mismatching signal matching

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detection of subspace distributed target in partial observation scenario with Rao test

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SIGNAL PROCESSING 2020年 166卷

作者： Xiao, Le Liu, Yimin Huang, Tianyao Wang, Lei Wang, Xiqin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

This paper deals with the problem of detecting a subspace distributed target obscured by disturbance consisting of clutter plus white noise. We focus on the partial observation scenario where some of the radar observations are missing, a phenomenon that usually caused by interference, spectrum sharing, compressed sampling, and so on. detection strategies are established based on the Rao test. Specifically, we first derive the Rao test with the assumption that the disturbance covariance matrix under the null hypothesis is known. Then, the unknown covariance matrix in the test statistic is replaced with a suitable estimate to make the detector adaptive. At the estimation stage, two cases are considered, involving with and without disturbance only secondary data. The estimate of the disturbance covariance matrix is obtained by solving an optimization problem in the respective case that considers both the likelihood maximization and low-rank property of the clutter covariance matrix. Simulation results are presented to verify the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed target detection Subspace target Partial observation Rao test

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Bayesian distributed target Detectors in Compound-Gaussian Clutter Against Subspace Interference with Limited Training Data

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REMOTE SENSING 2025年第5期17卷

作者： Xing, Kun Cao, Zhiwen Liu, Weijian Cui, Ning Wang, Zhiyu Yu, Zhongjun Yu, Faxin Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Peoples R China

In this article, the problem of Bayesian detecting rank-one distributed targets under subspace interference and compound Gaussian clutter with inverse Gaussian texture is investigated. Due to the clutter heterogeneity, the training data may be insufficient. To tackle this problem, the clutter speckle covariance matrix (CM) is assumed to obey the complex inverse Wishart distribution, and the Bayesian theory is utilized to obtain an effective estimation. Moreover, the target echo is assumed to be with a known steering vector and unknown amplitudes across range cells. The interference is regarded as a steering matrix that is linearly independent of the target steering vector. By utilizing the generalized likelihood ratio test (GLRT), a Bayesian interference-canceling detector that can work in the absence of training data is derived. Moreover, five interference-cancelling detectors based on the maximum a posteriori (MAP) estimate of the speckle CM are proposed with the two-step GLRT, the Rao, Wald, Gradient, and Durbin tests. Experiments with simulated and measured sea clutter data indicate that the Bayesian interference-canceling detectors show better performance than the competitor in scenarios with limited training data.

关键词： distributed target detection Bayesian theory subspace interference compound Gaussian clutter

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Persymmetric adaptive detection of distributed targets in compound-Gaussian sea clutter with Gamma texture

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SIGNAL PROCESSING 2018年 152卷 340-349页

作者： Liu, Jun Liu, Sha Liu, Weijian Zhou, Shenghua Zhu, Shengqi Zhang, Zi-Jing Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Anhui Peoples R China Wuhan Elect Informat Inst Wuhan 430019 Hubei Peoples R China

We consider the problem of detecting a distributed target in compound-Gaussian sea clutter with Gamma distributed texture components. We design the Rao, Wald, and generalized likelihood ratio test (GLRT) detectors according to the two-step method: the first step is to obtain the Rao, Wald tests and GLRT on the assumption that the texture or/and covariance matrix structure are known;then the maximum a posteriori probability estimate of the clutter texture and the fixed point estimate of covariance matrix exploiting persymmetry are employed to replace the known texture and covariance matrix in the tests derived in the first step. Remarkably these proposed detectors ensure constant false alarm rate with respect to the covariance matrix structure. The effectiveness of the proposed detectors is verified by using simulated and real sea clutter data. (C) 2018 Published by Elsevier B.V.

关键词： distributed target detection Compound-Gaussian clutter Gamma distribution Rao test Wald test Generalized likelihood ratio test

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A tunable detector for distributed targets when signal mismatch occurs

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ELECTRONICS LETTERS 2021年第15期57卷 594-596页

作者： Cui, Yufeng Liu, Weijian Du, Qinglei Liu, Jun Wang, Yong-Liang Wuhan Elect Informat Inst Wuhan Peoples R China Univ Sci & Technol China Elect Engn & Informat Sci Hefei Peoples R China

Aiming at the problem of distributed target detection when there is signal mismatch, this paper proposes a tunable detector, which is characterised by a tunable parameter. The proposed detector can realise the flexible detection of mismatch signals by adjusting the tunable parameter, and hence the directivity (robustness or selectivity to mismatched signals) of the tunable detector can be smoothly adjusted. In addition, the tunable detector can obtain approximately the same detection performance as the corresponding generalised likelihood ratio test in the absence of signal mismatch. The proposed tunable detector also possesses the constant false alarm rate property. Numerical examples illustrate the effectiveness of the proposed detector.

关键词： tunable parameter mismatched signals Other topics in statistics signal mismatch Linear algebra (numerical analysis) radar detection maximum likelihood estimation tunable detector mismatch signals object detection distributed target detection Radar equipment, systems and applications covariance matrices Signal detection

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