PROJECT TITLE :

Sparse Representation-Based ISAR Imaging Using Markov Random Fields

ABSTRACT:

To encourage the continuity of the target scene, a unique sparse illustration (SR)-primarily based inverse synthetic aperture radar (ISAR) imaging algorithm is proposed by leveraging the Markov random fields (MRF). The ISAR imaging problem is reformulated in an exceedingly Bayesian framework where correlated priors are used for the hidden variables to enforce the continuity of target scene. To additional enforce the nonzero or zero scatterers to cluster in a very spatial consistent manner, the MRF is used as the prior for the support of the target scene. To surmount the issue of calculating the posterior thanks to the imposed correlated priors and the MRF, variational Bayes expectation-maximization (VBEM) technique is employed to simultaneously approximate the posterior of the hidden variables and estimate the model parameters of the MRF. The convergence of the tactic is well diagnosed by commonly used stopping criterion. Both the artificial and therefore the experimental results demonstrate that the proposed algorithm will achieve substantial enhancements in terms of preserving the weak scatterers and removing noise parts over alternative reported SR-based ISAR imaging algorithms.