Atomic Norm Minimization for Maneuvering Target Parameter Separated Estimation in STAP

In this paper, a parameter estimation algorithm for moving targets based on atomic norm space-time adaptive processing is proposed. First, the acceleration and velocity parameters are separated by applying bilinear transformation to the constructed joint sparse recovery data. Next, the low-rank matrix recovery theory is used to sparsely recover the two parameters in their respective continuous atom sets, and the corresponding parameter subspace is obtained by using positive semidefinite programming and the alternating direction multiplier method. Finally, the corresponding parameter estimates are solved by the matrix bundle method. The proposed algorithm avoids the problem of estimation performance degradation caused by dictionary mismatch in the existing sparse recovery methods. Experiments show that in the case of high signal-to-noise ratio, the estimation performance is better than that of the existing sparse recovery space-time moving target parameter estimation methods based on fixed discretization dictionaries.