Deterministic maximum likelihood direction of arrival estimation using GSA
Abhinav Sharma, Sanjay Kumar Mathur · 2016
Direction of Arrival (DOA) estimation is an important problem in the field of array signal processing. There are many spectral and eigen structure algorithms for estimating direction of narrow band sources. Maximum Likelihood (ML) method is an efficient DOA estimation technique compared to other eigen structure based methods mainly due to its superior statistical performance. In this paper, we use Gravitational Search Algorithm (GSA) to find deterministic ML solution by optimizing a complex nonlinear multimodal function over a high dimensional space in linear arrays. Simulation results show that GSA algorithm gives better performance at lower SNR compared to other heuristic approaches like Particle Swarm Optimization (PSO) and conventional methods like Capon, MUSIC and ESPRIT. The performance of the algorithms are judged in terms of Root Mean Square Error (RMSE) and probability of resolution.