Adaptive Bayesian detection for multiple‐input multiple‐output radar in compound‐Gaussian clutter with random texture. Issue 4 (1st April 2016)

• Record Type: Journal Article
• Authors: Kong, Lingjiang; Li, Na; Cui, Guolong; Yang, Haining; Liu, Qing Huo
• Is Part Of: IET radar, sonar & navigation. Volume 10:Issue 4(2016)
• Page Start: 689
• Page End: 698
• Publication Date: 2016-04-01
• Subjects: MIMO radar -- radar detection -- Bayes methods -- radar clutter -- gamma distribution; multiple‐input multiple‐output radar -- compound‐Gaussian clutter -- random texture -- adaptive Bayesian detection -- Gamma distribution -- inverse Gamma distribution -- inverse complex Wishart distribution -- generalised likelihood ratio tests; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2015.0241
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : 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.