In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed.
Compressive Sensing-Based Joint Range-Doppler and Clutter Estimation
IEEE Transactions on Aerospace and Electronic Systems ; 55 , 6 ; 3207-3217
2019-12-01
1296819 byte
Article (Journal)
Electronic Resource
English