Characterizing partition detectors with stationary and quasi-stationary Markov dependent data

Multisensor data from sonar arrays are usually temporally and spatially dependent. In addition, in some environments the noise field in which the sonar system must operate is contaminated by impulsive and non-Gaussian interference. A natural way to include these effects is to formulate a statistic based upon the likelihood ratio. However, implementing a multisensor likelihood ratio statistic in its full generality is a formidable task. Our approach to solving the implementation complexity problem is to partition the amplitude of each sensor output into
m
intervals and model the resulting data as a finite Markov chain. Certain performance measures for fixed-sample-size and sequential detection statistics based on Markov chains are considered.