A Low Computational Complexity SML Estimation Algorithm of DOA for Wireless Sensor Networks

• Published in: International Journal of Distributed Sensor Networks
• Main Authors: Faming Gong, Haihua Chen, Shibao Li, Jianhang Liu, Zhaozhi Gu, Masakiyo Suzuki
• Format: Article in Journal/Newspaper
• Language: English
• Published: Hindawi - SAGE Publishing 2015
• Subjects: Electronic computers. Computer science; QA75.5-76.95; DML
• Online Access: https://doi.org/10.1155/2015/352012; https://doaj.org/article/6d8cd5ed76bb47b1b0f0c0ba1bf54c4a

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.