KEYWORDS: Sensors, Signal detection, Environmental sensing, Statistical analysis, Monte Carlo methods, Signal processing, Interference (communication), Data modeling, Signal to noise ratio, Matrices
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.