Efficient coverage criterion for accurate target tracking using cooperative wireless sensor networks

This paper deals with the problem of cooperative tracking using large groups of sensor nodes. A Kalman filter-like estimator is implemented and tested for this purpose. The focus of this paper is to examine the effect of the sensor density in the monitored area on the accuracy of the tracking results. The work is mainly motivated by the fact that the target position information issued by a sensor node necessarily passes across other sensors before reaching the final destination. The uncertainty in the relative localization process (between sensor nodes) therefore plays an important role in the accuracy of the final target position estimation. To address this issue, we investigate the improvement in tracking efficiency per additional sensor node as the coverage of the monitored region increases. Moreover, we give the expression of the upper bound on the target positioning uncertainty increase rate as a function of the sensor density, the intrinsic localization uncertainty for each sensor, and the accuracy of the relative sensor localization system. Thorough simulations have been conducted in order to validate the analytical results using concrete test cases.