Array shape self-calibration using particle swarm optimization and decaying diagonal loading

Calibration of array shape error is a key issue for most existing direction finding algorithms. In this paper, we propose a new procedure that employs particle swarm optimization (PSO) and decaying diagonal loading (DDL) techniques to optimize the unconditional maximum likelihood (UML) function for array self-calibration. The proposed method is able to self- calibrate large array shape error. Moreover, it outperforms previous ones in terms of mean squared error that attains Cramer-Rao bound. An example of 5-sensor and 3-source geometry is used to demonstrate the efficacy of the proposed method.