Parametric sensor array calibration using measured steering vectors of uncertain locations

We consider the problem of sensor array calibration using a set of unique measured steering vectors of uncertain locations to estimate the unknown deterministic array perturbation parameters in a maximum likelihood framework. The array perturbations are parameterized by the sensor locations, mutual coupling coefficients, and receiver channel mismatch. We introduce a hybrid optimizer based on the amalgamation of gradient-based algorithms and the genetic algorithm. This optimizer is capable of coping with the problem of local extrema attractors, particularly initial estimates with large deviations from their true values. Numerical examples are provided to demonstrate the effectiveness and behavior of the proposed algorithms