Comparison of filtering techniques for simultaneous localization and tracking

Target tracking is one of the most important applications for wireless sensor networks (WSNs). It is usually assumed that the knowledge of the sensor nodes' position is known precisely. However, practically nodes are randomly deployed without prior knowledge about their own positions. In this situation, simultaneous localization and tracking (SLAT) is necessary and is receiving more and more research interest during the last few years. In this paper, several popular and practical filtering techniques are reviewed and compared for the problem of SLAT, including extended Kalman filtering (EKF), unscented Kalman filtering (UKF), and interactive multiple model (IMM). Simulation examples are included to demonstrate the superiority and shortcoming of each method. Results show that compared with other methods, IMM based on UKFs has better accuracy in both localization and tracking, as well as higher robustness.