Compressive sensing for very high frequency radar with application to low-angle target tracking under multipath interference

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Generally, the response at the receiver is contributed by the echoes of target and its image. Such sparsity of signals in the elevation direction provides a foundation for parameter estimation based on compressive sensing with parameterized dictionary. As the dictionary atoms are multi-parameter, it may increase the dimension of the parameterized dictionary. This may increase the cost of computation and storage violently. Aiming at this problem, a novel compressive sensing based algorithm, combined with alternative optimization and dictionary refinement, is proposed in this paper. The algorithm is based on the criterion of maximizing the correlation coefficient of target signal component and the atoms of a parameterized dictionary, where the dictionary grids can be iteratively refined to estimate the parameters more precisely. Numerical results based on simulated data and real data show the efficiency of the proposed algorithm.