Coherent Radar Detection in Compound-Gaussian Clutter: Clairvoyant Detectors

被引:23
作者
Sangston, K. James [1 ,2 ]
Farina, Alfonso [3 ]
机构
[1] Georgia Tech Res Inst, Sensors & Electromagnet Applicat Lab, Smyrna, GA 30701 USA
[2] GTRI, SEAL, 7220 Richardson Rd, Smyrna, GA 30080 USA
[3] Via Helsinki 14, I-00144 Rome, Italy
关键词
COVARIANCE-MATRIX ESTIMATION; SPHERICALLY INVARIANT NOISE; RESOLUTION SEA CLUTTER; K-DISTRIBUTED CLUTTER; OPTIMUM DETECTION; STATISTICAL-ANALYSIS; SIGNAL-DETECTION; WEIBULL CLUTTER; ADAPTIVE DETECTION; CFAR DETECTION;
D O I
10.1109/MAES.2016.150132
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
This paper provides a historical and tutorial overview of coherent radar target detection in compound-Gaussian clutter and offers some new perspectives and avenues of research in this challenging area. It begins with a brief introduction that motivates the need to develop statistical models of non-Gaussian clutter and then reviews some of the physical ideas that led to modeling multivariate radar clutter statistics by the compound-Gaussian model. With this starting point, the paper then reviews a series of ideas that have been developed to describe clairvoyant detectors in such clutter. The term "clairvoyant" refers to the assumption that the properties of the clutter are assumed to be known. In a practical scenario, this assumption does not hold and adaptive techniques are needed to estimate clutter properties and implement the detector. Such techniques are guided however by the appropriate clairvoyant detector structures and hence it is proper to start by studying these detectors. As part of this review, the paper offers new ways of looking at this problem that suggest new research topics. This review is limited to the problem of clairvoyant detection in which the relevant properties of the clutter are assumed to be known. Adaptive detection in compound-Gaussian clutter will be the topic of a subsequent tutorial that the authors are preparing.(1)
引用
收藏
页码:42 / 63
页数:22
相关论文
共 114 条
[1]   Order estimation and discrimination between stationary and time-varying (TVAR) autoregressive models [J].
Abramovich, Yuri I. ;
Spencer, Nicholas K. ;
Turley, Michael D. E. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2007, 55 (06) :2861-2876
[2]  
Abramovich YI, 2013, IEEE T SIGNAL PROCES, V61, P5807, DOI 10.1109/TSP.2013.2272924
[3]  
[Anonymous], 7986 NRL
[4]  
[Anonymous], 2012, Signal Detection in Non-Gaussian Noise
[5]  
ANTONOV OY, 1967, RADIO ENG ELECTRON P, V12, P541
[6]  
ANTONOV OY, 1967, RADIO ENG ELECTRON P, V12, P727
[7]   Maximum likelihood estimation for compound-Gaussian clutter with inverse gamma texture [J].
Balleri, Allessio ;
Nehorai, Arye ;
Wang, Jian .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2007, 43 (02) :775-780
[8]   Knowledge-Aided Covariance Matrix Estimation and Adaptive Detection in Compound-Gaussian Noise [J].
Bandiera, Francesco ;
Besson, Olivier ;
Ricci, Giuseppe .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2010, 58 (10) :5390-5396
[9]   Covariance matrix estimation with heterogeneous samples [J].
Besson, Olivier ;
Bidon, Stephanie ;
Tourneret, Jearl-Yves .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (03) :909-920
[10]   Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach-Part 2: The Under-Sampled Case [J].
Besson, Olivier ;
Abramovich, Yuri I. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (23) :5819-5829