An improved incremental least-mean-squares algorithm for distributed estimation over wireless sensor networks. (April 2017)

• Record Type: Journal Article
• Authors: Wu, Mou; Tan, Liansheng; Yang, Rong; Wan, Runze
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 4(2017)
• Publication Date: 2017-04
• Subjects: Distributed estimation -- incremental least mean squares -- steepest descent -- wireless sensor networks -- target localization; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147717703967
• Languages: English
• ISSNs: 1550-1329

Abstract

Distributed parameter estimation problem has attracted much attention due to its important foundation of some applications in wireless sensor networks. In this work, we first investigate the performance tradeoff between existing incremental least-mean-squares algorithm and traditional steepest-descent algorithm from the aspects of initial convergence rate, steady-state convergence behavior, and oscillation, and present the motivations for improvements. Thereby we propose an improved incremental least-mean-squares distributed estimation algorithm that starts from the incremental least-mean-squares algorithm and works toward the objective of improvement on initial convergence rate and steady-state performance. The proposed algorithm meets the requirements of diminishing step size for wireless sensor networks without any increase in communication overhead and the mean stability condition is derived for a practical guideline. A target localization model in wireless sensor networks is used to illustrate the effect of the proposed algorithm. Simulation results confirm the performance improvement of our method, as well as the effectiveness of application in wireless sensor networks.