Range-Doppler imaging via sparse representation

We pose the range-Doppler imaging problem as a two-dimensional sparse signal recovery problem with an overcomplete basis. The resulting optimization problem can be solved using both ℓ0 and ℓ1 norm minimization algorithms. Algorithm performance and estimation quality are illustrated using artificial data set, where targets are close to each other and target SNR is low. We show that accurate target location is achieved with high resolution. In particular, compared to other state-of-art algorithms, the proposed approach is shown to achieve more robustness in noisy environment with limited data.