Target tracking using energy based detections

Abstract

Energy based detection measures sensor received signal strength (RSS) transmitted from a target. In this paper, we propose a new approach for estimating a moving target trajectory over a sensor field via energy based detections as an alternative to trilateration positioning or nonlinear estimation. In 2D case, possible target locations described by a RSS ratio from two sensors are approximated using a set of Gaussian random variables which are refereed to as location measurements. At each data collection time, several sets of such measurements can be found from RSS ratios which are due to multiple sensor detections. A track splitting filter is used to perform either measurement fusion and target state estimation using these measurements. The RSS ratio data mapping via Gaussian density approximation plays a key role in the proposed target tracking method and is robust in the sense that it can tolerate larger RSS noise and using additional sensor detections to improve tracking performance over trilateration based techniques. The effectiveness of the propose method is demonstrated via an example of tracking a moving target over a sensor network of small acoustic sensors.