Multichannel adaptive signal detection in space-time colored compound-gaussian autoregressive processes

Qi Xu,Xiaochuan Ma,Bo Shi,Chengpeng Hao,Shefeng Yan

doi:10.1186/1687-6180-2012-69

Abstract

Abstract In this article, we consider the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally colored compound-Gaussian disturbance. By modeling the disturbance as a multichannel autoregressive (AR) process, we first derive a parametric generalized likelihood ratio test against compound-Gaussian disturbance (CG-PGLRT) assuming that the true multichannel AR parameters are perfectly known. For the two-step GLRT design criterion, we combine the multichannel AR parameter estimation algorithm with three covariance matrix estimation strategies for compound-Gaussian environment, then obtain three adaptive CG-PGLRT detectors by replacing the ideal multichannel AR parameters with their estimates. Owing to treating the random texture components of disturbance as deterministic unknown parameters, all of the proposed detectors require no a priori knowledge about the disturbance statistics. The performance assessments are conducted by means of Monte Carlo trials. We focus on the issues of constant false alarm rate (CFAR) behavior, detection and false alarm probabilities. Numerical results show that the proposed adaptive CG-PGLRT detectors have dramatically ease the training and computational burden compared to the generalized likelihood ratio test-linear quadratic (GLRT-LQ) which is referred to as covariance matrix based detector and relies more heavily on training.

Highlights

In an airborne radar system, space-time adaptive processing (STAP) has been widely used in radar target detection; see [1-4] and references therein
Motivated by the previous studies, the main purpose of this article is to derive a parametric generalized likelihood ratio test (GLRT) (PGLRT) for detecting a multichannel signal in the presence of compound-Gaussian disturbance modeled as a multichannel AR process
Note that the normalized parametric adaptive matched filter (NPAMF), originally developed in [8,14] for compound-Gaussian environment, is closely related to the CG-PGLRT detector but replaces true Q and AH with their estimates obtained from the training signals

Summary

Introduction

In an airborne radar system, space-time adaptive processing (STAP) has been widely used in radar target detection; see [1-4] and references therein. In [15], based on approximating the disturbance spectrum with a multichannel AR model of low order, a parametric adaptive matched filter (PAMF) for STAP detection was presented for multichannel system in Gaussian environment. For the corresponding problem in compound-Gaussian environment, a non-Gaussian parametric adaptive matched filter (NG-PAMF) has been derived in [24]. This test involves explicit knowledge of the disturbance statistics, which are not always available. Motivated by the previous studies, the main purpose of this article is to derive a parametric GLRT (PGLRT) for detecting a multichannel signal in the presence of compound-Gaussian disturbance modeled as a multichannel AR process. We first derive the model-based parametric GLRT in compound-Gaussian environment (CG-PGLRT), which possesses the perfect knowledge about the multichannel AR parameters. C denotes the complex number field and det{} takes the determinant of a matrix

Problem statement and signal model

CG-PGLRT detector for known AR parameters

Adaptive CG-PGLRT detector for unknown AR parameters

Conclusions