Representing sonar/radar returns as a Markov process

The author formulates the multiple transmission application in terms of a sequential detection problem based on the likelihood ratio. The first application is for a Rayleigh fluctuating target. The likelihood ratio is constructed based on probability density functions. This gives the optimum receiver for the assumed conditions. A more general model based on Markov process is considered next. In this case the current return is correlated with past returns. It is shown that correlated returns improve detection performance. Extending this model further, correlated returns with non-Gaussian statistics are formulated using mixture densities