Traditional subspace detection for the second-order Gaussian (SOG) model signal is generally considered in the homogeneous or partially homogeneous environments. This paper addresses the problem of the subspace detection for the SOG signal in the presence of the nonhomogeneous noise whose covariance matrices in the primary and secondary data are assumed to be random, with some appropriate distributions. Within this nonhomogeneous framework, a novel adaptive subspace detector is proposed in terms of an approximate generalized likelihood ratio test (AGLRT) and the Gibbs sampling strategy. The numerical result evaluates the performance of the subspace detector with Monte Carlo method under nonhomogeneity.

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335D (29 August 2016); doi: 10.1117/12.2245158

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)