A refined genetic algorithm for accurate and reliable DOA estimation with a sensor array

M. Li, Y. Lu

Research output: Contribution to journalArticle

20 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)533-547
Number of pages15
JournalWireless Personal Communications
Issue number2
Publication statusPublished - Oct 2007



  • array signal processing
  • direction finding
  • genetic algorithms
  • maximum likelihood

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