Constrained Weighted Least Square Optimization for Vehicle Position Tracking

This paper describes an effective method for vehicle positioning estimation for range-based wireless network. The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time of arrival (TOA), received signal strength (RSS), time difference of arrival (TDOA) and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the vehicles. In this paper, Constrained weighted least squares (CWLS) for vehicle position tracking approach with TDOA technique describes the optimized ranging measurement for the vehicles. Kalman filter is used for smoothing range data and mitigating the NLOS errors. In proposed algorithm positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The paper presents a simple recursive model by using time difference of arrival based position 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 positioning 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 position corrections back to the Kalman Filter. It compensates for the measured geometrical position and decreases random error influence to the position precision. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.