Abstract
In the important domain of array shape calibration, the near-field case poses a challenging problem due to the array response complexity induced by the range effect. In this paper, near-field calibration is carried out using an unconditional maximum likelihood (UML) estimator. Its objective function is optimized by the particle swarm optimization (PSO) algorithm. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. UML estimator offers Cramer-Rao bound (CRB)-attaining accuracy. The direct optimization by PSO without approximation makes the estimator applicable to the entire near-field. In addition, PSO is free of the initialization problem from which the local optimization algorithms suffer. Numerical simulations demonstrate the CRB-attaining results at SNR as high as 60 dB.
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