Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture

• Published in: IET radar, sonar & navigation Vol. 10; no. 4; pp. 689 - 698
• Main Authors: Kong, Lingjiang, Li, Na, Cui, Guolong, Yang, Haining, Liu, Qing Huo
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.04.2016
• Subjects: adaptive Bayesian detection; Bayes methods; Bayesian analysis; Clutter; compound‐Gaussian clutter; gamma distribution; generalised likelihood ratio tests; Inverse; inverse complex Wishart distribution; inverse Gamma distribution; MIMO radar; multiple‐input multiple‐output radar; Navigation; Probability distribution functions; Radar; radar clutter; radar detection; random texture; Surface layer; Texture; radar detection; inverse complex Wishart distribution; radar clutter; MIMO radar; gamma distribution; multiple-input multiple-output radar; adaptive Bayesian detection; Bayes methods; compound-Gaussian clutter; random texture; generalised likelihood ratio tests; inverse Gamma distribution
• ISSN: 1751-8784; 1751-8792
• DOI: 10.1049/iet-rsn.2015.0241

In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure, but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow Gamma distribution or inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, two generalised likelihood ratio tests are derived. Finally, they evaluate the capabilities of the proposed detectors against compound-Gaussian clutter as well as their superiority with respect to some existing techniques.