TDoA based UGV Localization using Adaptive Kalman Filter Algorithm

Abstract

The measurement with a signal of time difference of arrival (TDoA) is a widely used technique in source localization. However, this method involves much nonlinear calculation. In this paper, we propose a method that needs less computation for UGV location tracking using extended Kalman filtering based on non linear TDoA measurements. To overcome the inaccurate results due to limited linear approximation, this paper suggests a position estimation algorithm based upon an adaptive fading Kalman filter. The adaptive fading factor enables the estimator to change the error covariance according to the real situation. Through the comparison with other analytical methods, simulation results show that the proposed localization method achieves an improved accuracy even with reduced computational efforts.