Genetic algorithm based maximum likelihood DOA estimation
Authors:
- M. LiMLM. Li
- Y. LuYLY. Lu
- 1 October 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The maximum likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. We present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and multiple signal classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between the ML technique and MUSIC is also investigatedKeywords
This publication has 8 references indexed in Scilit:
- Maximum likelihood parameter estimation by simulated annealing2003
- Maximum-likelihood DOA estimation by data-supported grid searchIEEE Signal Processing Letters, 1999
- Array failure correction with a genetic algorithmIEEE Transactions on Antennas and Propagation, 1999
- Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerationsProceedings of the IEEE, 1997
- Performance study of conditional and unconditional direction-of-arrival estimationIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Maximum likelihood methods for direction-of-arrival estimationIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- MUSIC, maximum likelihood, and Cramer-Rao boundIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Maximum likelihood localization of multiple sources by alternating projectionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988