Dynamic Location Estimation by Kalman Filter

This paper describes an effective method for dynamic location estimation by Kalman Filter for range-based wireless network. The problem of locating a mobile terminal has received significant attention in the field of wireless communications. In this paper, Kalman Filter with TDOA technique describes the ranging measurement tracking approach. Kalman filter is used for smoothing range data and mitigating the NLOS errors. The paper presents a simple recursive model by using time difference of arrival based location measurement and incorporating state equality constraints in the Kalman filter. From the process of Kalman filtering, the standard deviation of the observed range data can be calculated and then used in NLOS/LOS hypothesis testing. The proposed recursive locating algorithm, compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, will be comparatively more robust to measurement errors because it updates the technique that feeds the location corrections back to the Kalman Filter. It compensates for the measured geometrical location and decreases random error influence to the location precision. Simulation results show that the proposed location estimation algorithm can improve the accuracysignificantly.