PROJECT TITLE:

New Sparse-Promoting Prior for the Estimation of aRadar Scene with Weak and Strong Targets - 2016

ABSTRACT:

In this paper, we have a tendency to take into account the matter of estimating a sign of interest embedded in noise employing a sparse signal illustration (SSR) approach. This problem has relevancy in many radar applications. In specific, estimating a radar scene consisting of targets with wide amplitude vary can be challenging since the sidelobes of a robust target can disrupt the estimation of a weak one. At intervals a Bayesian framework, we have a tendency to gift a replacement sparse-promoting prior designed to estimate this specific kind of radar scene. The main strength of this new prior lies in its mixed-type structure which decorrelates sparsity level and target power, along with in its subdivided support which enables the estimation method to span the full target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on artificial and semiexperimental radar information and compared to state-of-the-art algorithms.