Maximum likelihood (ML) direction-of-arrival (DOA) estimation algorithm is a nearly optimal technique. In this paper, we present a modified and refined genetic algorithm (GA) to find the exact solutions to the complex, multi-modal, multivariate and highly nonlinear likelihood function. With the newly introduced features such as intelligent initialization and the emperor-selective mating scheme, carefully selected crossover and mutation operators, and fine-tuned parameters such as the population size, the probability of crossover and mutation, the GA-ML estimator achieves fast global convergence. The GA-ML estimator has been compared with various DOA estimation methods in a variety of scenarios, and the simulation results demonstrate that in most scenarios the proposed GA-ML estimator is the fastest and its performance is the best among popular ML-based DOA estimation methods.
- array signal processing
- direction finding
- genetic algorithms
- maximum likelihood
Li, M., & Lu, Y. (2007). A refined genetic algorithm for accurate and reliable DOA estimation with a sensor array. Wireless Personal Communications, 43(2), 533-547. https://doi.org/10.1007/s11277-007-9248-5