A maximum entropy framework for space-time adaptive processing

We address the problem of devising and analyzing radar processors for space-time adaptive processing applications. We first define a class of adaptive filters based on the approximation of the overall disturbance with a multichannel autoregressive process. Thus we focus on a specific element of the family which exploits the modified square-root normalized maximum entropy algorithm for estimating the process coefficients. The performance assessment highlights the fast convergent capabilities of the novel processor, which for a limited number of training data, provides performances very close to the optimum filter.