Variational Bayesian inference for sparse representation of migrating targets in wideband radar
Stéphanie Bidon, Anaïs Tamalet, Jean‐Yves Tourneret · 2013
One of the distinguishing feature of a wideband radar is its fine range resolution. Accordingly moving targets observed by such system are prone to migrate during the coherent processing interval. This range walk offers additional information about the target velocity that can be used to alleviate velocity ambiguity. In a former work, we presented a Bayesian algorithm giving a non-ambiguous and sparse representation of migrating targets. The estimation method was based on a Monte-Carlo Markov chain (MCMC) method. We propose here an algorithm allowing the computational cost of the previous MCMC method to be significantly reduced, at the price of a small performance degradation.