A New Incremental Optimization Algorithm for ML-Based Source Localization in Sensor Networks

Author

Shi, Qingjiang ; He, Chen

Author_Institution

Shanghai Jiao Tong Univ., Shanghai

Volume

15

fYear

2008

fDate

6/30/1905 12:00:00 AM

Firstpage

45

Lastpage

48

Abstract

A new incremental optimization algorithm called normalized incremental subgradient (NIS) algorithm is proposed in this letter, which can be used for distributed maximum likelihood estimation (MLE). Its convergence with a diminishing stepsize has been proved and analyzed theoretically. We then apply the NIS algorithm to the energy-based sensor network source localization problem where the decay factor of the energy decay model is unknown. Simulation results show it can achieve very high estimation performance, which is only somewhat lower than that of the centralized localization method based on global optimization techniques, but with hundreds of times lower computational complexity than the centralized method.

Keywords

array signal processing; gradient methods; maximum likelihood estimation; wireless sensor networks; decay factor; distributed maximum likelihood estimation; energy decay model; energy-based sensor network; incremental optimization; normalized incremental subgradient; source localization; Acoustic sensors; Computational complexity; Computational modeling; Convergence; Energy measurement; Helium; Maximum likelihood estimation; Optimization methods; Signal processing algorithms; Wireless sensor networks; Centralized method; distributed maximum likelihood estimation; energy-based source localization; normalized incremental subgradient algorithm; sensor network;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2007.911180

Filename

4418409