Comparison of three Kalman filters for an indoor passive tracking system

Wireless sensor networks can be used for the localization and tracking of moving targets. However, the range measurements are noisy and Kalman filters are frequently used to improve the tracking accuracy. Three different tracking algorithms, namely, a standard Kalman filter (SKF), an extended Kalman filter (EKF), and a modified Kalman filter (MKF) are empirically studied in terms of accuracy and latency for a range-based indoor tracking system. The experimental results show that the filtering techniques improve the tracking accuracy when the target is moving rapidly. However, different forms of Kalman filters introduce different levels of latency which affects the real-time tracking performance.