NDLI: Covariance-free non-homogeneity STAP detector in compound Gaussian clutter based on robust statistics

Content Provider	IET Digital Library
Author	Abouelfadl, Ahmed A. Psaromiligkos, Ioannis Champagne, Benoit
Abstract	Space-time adaptive processing (STAP) detects targets by computing adaptive weight vectors for each cell under test using its covariance matrix, as estimated from surrounding secondary cells. In this study, the non-homogeneity detector (NHD) excludes the anomalous secondary cells that adversely affect the detection performance. The existing robust NHDs require estimating the covariance matrix of each secondary cell, which hinders their implementation in modern radars with large-dimensional range cells. In this study, the authors propose a new low-complexity NHD that is suitable for highly correlated clutter environments with both Gaussian and non-Gaussian heavy-tailed distributions. The proposed detector, which is based on the projection depth function from the field of robust statistics, features a non-parametric and covariance-free test statistic. As a result, its computational complexity is much lower than that of current NHDs, such as the widely used normalised adaptive matched filter (NAMF) detector, especially for large-dimensional range cells. In Monte Carlo simulations with different clutter distributions and radar system configurations, the proposed detector shows comparable performance to that of NAMF. The low complexity and robust performance of the new detector make it particularly attractive for real-time applications.
Starting Page	2107
Ending Page	2119
Page Count	13
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 12, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0201
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-08-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Weight Vector Computation Anomalous Secondary Cell Clutter Distributions Compound Gaussian Clutter Computational Complexity Correlated Clutter Environments Covariance Matrices Covariance Matrix Covariance-free Test Statistics Filtering Method in Signal Processing Gaussian Distribution Gaussian Heavy-tailed Distribution Large-dimensional Range Cell Linear Algebra Low-complexity NHD Matched Filter Monte Carlo Method Monte Carlo Simulation Method NAMF Detector NonGaussian Heavy-tailed Distribution Nonhomogeneity Detector Nonhomogeneity STAP Detector Normalised Adaptive Matched Filter Detector Numerical Analysis Object Detection Projection Depth Function Radar Clutter Radar Detection Radar Equipment Radar System Configurations Signal Detection Space-time Adaptive Processing System And Application Target Detection Vector
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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