Adaptive two-step Bayesian MIMO detectors in compound-Gaussian clutter

• Published in: Signal processing 2019-08, Vol.161, p.1-13
• Main Authors: Li, Na, Yang, Haining, Cui, Guolong, Kong, Lingjiang, Huo Liu, Qing
• Format: Article
• Language: English
• Subjects: Adaptive detection; Bayesian detection; Compound-Gaussian clutter; Covariance matrix estimation; Maximum a posteriori estimator; Multiple-input multiple-output radar
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2019.03.008

•We study the adaptive target detection with MIMO radar in compound-Gaussian clutter.•Covariance matrix structure is assumed to be random with an appropriate distribution.•Two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution.•Three adaptive GLRTs are developed according to the ad hoc design procedure.•Three MAP estimators of covariance matrix structure are proposed using secondary data. The problem of adaptive target detection in compound-Gaussian clutter with unknown covariance matrix for multiple-input multiple-output (MIMO) radar is addressed in this paper. A set of secondary data for each receiver is assumed to be available, and the primary data and the secondary data own the same covariance matrix structure but different power levels (textures). Firstly, a Bayesian approach is proposed, where the structure is modeled as a random matrix with an appropriate distribution. Then, two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution. In this framework, three adaptive generalized likelihood ratio tests (GLRTs) are developed using the two-step design procedure. Finally, the capabilities of the proposed detectors and their superiority with respect to some existing techniques are evaluated via numerical simulations.