<title>Sequential collaborative processing for energy efficiency and fault tolerance in unattended ground sensor networks</title>

Abstract

Multiple target detection in sensor networks is a challenging problem since the signal captured by individual sensor nodes is normally a linear/nonlinear weighted mixture of geometrically diverse source signals. Independent component analysis (ICA) has been widely used to solve the source estimation problem, but most of the algorithms assume the number of sources is fixed and is equal to the number of observations, which generally is not the case in sensor networks. Even though several methods are put forward for source number estimation, traditional centralized schemes hinder their application in sensor networks due to extremely constrained resource and scalability issues. In this paper, a sequential source number estimation framework is developed, where we assume the sensor network has been self-organized into clusters. The determination of possible number of targets at a sensor is only based on its local observation and the estimation result received from its previous sensor. Therefore, raw data transmission is avoided and only small packets of partial estimation results are transmitted through the networks. Based on the local estimation generated within each cluster, a posterior probability fusion method based on the Bayes' theorem is derived. Experimental results show that using the sequential processing approach with probability fusion, the detection probability and reliability are improved over the centralized scheme while significantly reducing network traffic and conserving resources.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.