Improved focal underdetermined system solver method for radar coincidence imaging with model mismatch

Abstract

Radar coincidence imaging (RCI) is a staring imaging technique that originated from optical coincidence imaging. In RCI, the reference matrix needs to be computed precisely to reconstruct the image. However, it is difficult to exactly calculate the reference matrix as model mismatch existing in most applications. The signal model of RCI with model mismatch is derived. Based on a Bayesian framework and regularization method, an algorithm called regularization-focal underdetermined system solver (R-FOCUSS) is proposed to solve the RCI problem with model mismatch. In the proposed method, the scattering coefficients and the perturbation matrix can be calculated during the iterations, so the image can be reconstructed. A norm-ratio method is also proposed to determine the regularization parameters in the objective function, which makes the algorithm suitable for the situation, where the distributions of noise, model error, and target’s sparsity are unknown. The constrained Cramer–Rao bound for scatterer estimation is derived. Compared with some existing sparse reconstruction methods, R-FOCUSS is more robust, with a lower computation complexity. Results of numerical experiments demonstrate that the algorithm can achieve outstanding imaging performance and yields superior performance both in suppressing noise and in adapting to model mismatch.