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

• Published in: International Journal of Distributed Sensor Networks
• Main Authors: Gong, Faming, Chen, Haihua, Li, Shibao, Liu, Jianhang, Gu, Zhaozhi, Suzuki, Masakiyo
• Other Authors: Fundamental Research Funds for the Central University, China
• Format: Article in Journal/Newspaper
• Language: English
• Published: SAGE Publications 2015
• Subjects: DML
• Online Access: http://dx.doi.org/10.1155/2015/352012; http://downloads.hindawi.com/journals/ijdsn/2015/352012.pdf; http://downloads.hindawi.com/journals/ijdsn/2015/352012.xml; http://journals.sagepub.com/doi/pdf/10.1155/2015/352012

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.