Sidelobe suppression and super resolution for MIMO imaging radar

An algorithm for super resolution and sidelobe suppression is described for coherent collocated MIMO imaging radar. The algorithm modifies the coherent CLEAN method by performing a nonlinear least squares (NLLS) optimization in the beam domain. The NLLS optimization is performed at each step of the CLEAN algorithm, accounting for the mutual interference terms of closely spaced targets. This removes the requirement of a priori target information normally essential in NLLS optimization whilst also providing scope for super resolution. Frechet derivatives are used to generate the Hessian matrix for a Newton-Raphson iteration to solve the NLLS optimization. Particular attention is paid to convergence of this Newton-Raphson iteration to the global minimum. Inherently this large optimization is computationally expensive. Therefore a Dolph-Chebyshev weighting is presented as the optimum window for solving the NLLS optimization in a block matrix fashion. Finally numerical simulations are provided to demonstrate the proposed algorithms ability to take advantage of the improved SNR from operating in the beam domain.