Distributed DOA estimation using clustering of sensor nodes and diffusion PSO algorithm

This paper proposes a distributed DOA estimation technique using clustering of sensor nodes and distributed PSO algorithm. The sensor nodes are suited by clustered to act as random arrays. Each cluster estimates the source bearing by optimizing the Maximum Likelihood (ML) function locally with cooperation of other clusters. During the estimation process each cluster shares its best information obtained by Diffusion Particle Swarm Optimization (DPSO) with other clusters so that the global estimation is achieved. The performance of the proposed technique has been evaluated through simulation study and is compared with that of obtained by the centralized and decentralized MUltiple SIgnal Classification (MUSIC) algorithms and distributed in-network algorithm. The results demonstrate improved performance of the proposed method compared to others. However, the new method exhibits slightly inferior performance compared to the centralized Particle Swarm Optimization-Maximum Likelihood (PSO-ML) algorithm. Further the proposed method offers low communication overheads compared to other methods.