Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing

Abstract

The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment. Furthermore, we provide adaptive iteration BCS localization (AIBCSL) algorithm, which is based on BCS and will choose measurement sensors according to the environment adaptively with only an initial value, while other frameworks require prior knowledge such as target numbers to choose measurements. AIBCSL suppose that environment noise variance is identical in interested area in a short period of time and change measurement numbers until terminal condition is reached. To suppress noise, we optimize estimation result by energy threshold strategy (ETS), which takes that transmit energy of noise focused on single grid is much lower than signal into consideration. And multisnapshot BCS (MT-BCS) will be explained and lead to a good result in low SNR level situation.