Maximum Likelihood DOA Estimation Using Particle Swarm Optimization under Sensor Perturbation Conditions

Abstract

Considering that the estimation problem in which the direction of arrival (DOA) of sensor signals experiences array gains and positional perturbations exists within the code-division multiple-access (CDMA) system, this study employed two types of estimation functions, the maximum likelihood (ML) and weighted subspace fitting (WSF) functions. ML and WSF functions are complex non-linear, multimodal functions that feature highdimensional problem spaces and typically use calibrated arrays to estimate DOAs. Thus, to calibrate arrays, this study proposed an improved particle swarm optimization (PSO) method for calculating the ML and WSF functions, and identified the optimal solution for each function. The proposed methods do not require calibrated source signals and can estimate the sensor perturbations and DOAs of incident signals. The simulation results showed that the proposed estimator outperforms other estimation methods.