Adaptive Detection in Deterministic Subspace Interference Based on Wald-Like Test
Peiqin Tang, Hong Xu, Weijian Liu, Jun Liu, Yinghui Quan, Yong-Liang Wang
Abstract
In this article, we investigate the problem of distributed target adaptive detection in the presence of deterministic subspace interference and Gaussian noise, wherein the target signal and interference are assumed to lie in independent subspaces, and a set of independent and identically distributed training samples is used to learn the noise covariance matrix. In the context of the above assumption, three new adaptive detectors are proposed resorting to a Wald-like criterion in a homogeneous environment and a partially homogeneous environment. Sufficient experimental results obtained by using simulation data and real data collected from the IPIX radar indicate that the proposed Wald-like detectors can provide better detection performance than their competitors in some scenarios. Moreover, all these Wald-like detectors possess constant false alarm rate property.