Constrained Extended Kalman Filter for Target Tracking in Directional Sensor Networks

Abstract

The target tracking problem in directional sensor networks (DSNs) is attracting increasing attention. Unlike the traditional omnidirectional sensor, a directional sensor has a special angle of view. It can offer direction information rather than just the sensing signal measurement with respect to the detected target. The existing tracking approaches in DSNs always separately consider the direction and measurement information; they hardly promise the tracking performance of minimum variance. In this paper, the field of view of directional sensor is approximated to a rectangle; as such the constrained area in which the target is bound to be is constructed. Then, the target tracking problem is formulated as a constrained estimation problem, and a constrained extended Kalman filter (CEKF) tracking algorithm integrating the direction and measurement information is presented; its structural and statistical properties are rigorously derived. It is proved that CEKF is the linear unbiased minimum variance estimator, and CEKF can yield a smaller error covariance than the unconstrained traditional extended Kalman filter using only sensor measurements. Simulation results show that the CEKF has superior tracking performance for directional wireless networks.