Direction finding and array calibration based on maximal set of nonredundant cumulants

An algorithm for direction finding and array calibration based on the maximal set of nonredundant cumulants (MSNC) is presented in this paper. The algorithm utilizes all nonredundant information of the spatial fourth-order cumulants. It has been shown that the algorithm is insensitive to additive Gaussian noise and superior to conventional covariance-based DOA algorithms with reduced estimation variances and enhanced resolvability. The algorithm also circumvents the iteration between direction finding and array calibration, without the problem of convergence and with improved robustness. Simulation experiments demonstrate the potential performance of the proposed algorithm.