Adaptive CFAR detectors for subspace signal in uncertain rank-one interference and Gaussian noise

Abstract

This paper addresses the problem of detecting a subspace signal in the presence of rank-one interference and Gaussian noise. The interference steering vector is uncertain but confined to a known subspace of the observables, with its exact coordinate being unknown. Additionally, the interference subspace is linearly independent of the signal subspace. Given the unknown noise covariance matrix, we assume the availability of noise-only (training) data for estimation purposes. The covariance matrices of the test and training data are either identical, indicating a homogeneous environment (HE), or share a common structure with an unknown scaling factor, suggesting a partially HE (PHE). At the design stage, we employ both one-step and two-step generalized likelihood ratio tests (GLRTs) to derive two detectors for HE and one detector for PHE. These new detectors maintain the constant false alarm rate (CFAR) property. Furthermore, they are compared with existing detectors in terms of computational complexity and detection performance. Specifically, the computational complexity of the new detectors proposed in this paper is comparable to that of existing detectors. Extensive numerical experiments confirm that the new detectors consistently outperform existing ones in terms of detection performance.