On the Performance of Target Tracking Algorithms using Actual Localization Systems for Wireless Sensor Networks

In wireless sensor networks (WSNs), target tracking algorithms are often based on geographical information provided by a localization algorithm. However, the errors introduced by such algorithms may affect the performance of tasks that depend on such information. In this paper, we evaluate how errors resulting from actual localization algorithms affect two classical target tracking algorithms: the Kalman filter and the Particle filter. As a proof-of-concept, we choose two relevant localization algorithms for WSNs. The first is the RPE (Recursive Position Estimation), a pioneer iterative solution, and the second is the DPE (Directed Position Estimation), another iterative solution that evolved from the RPE reducing errors and the cost of its predecessor. Our results clearly indicate that the tracking algorithms successfully filter the noise introduced by the target-node distance estimation. However, they fail to eliminate the errors introduced by localization algorithms.