Sensor array calibration using measured steering vectors of uncertain location

We present a maximum likelihood approach for calibrating sensor arrays in the presence of mutual coupling, channel gain and phase mismatch and array geometry uncertainties using measured steering vectors of uncertain locations. The estimated perturbation parameters is used to calibrate the array manifold, hence enabling many high resolution array processing algorithms to attain their potential advantages. We present two methods for optimizing the highly nonlinear and multimodal ML cost function. The first method is a linearized local gradient search algorithm. The second method is derived from combining the fast local search of gradient methods with the nonlinear global search ability of the genetic algorithm. The resulting hybrid optimizer is both fast and globally converging. Simulation results are presented to illustrate the usefulness of the proposed approach.