Mobile localization via unscented Kalman filter with sensor position uncertainties

Abstract

This paper investigates the localization problem of a mobile source based on time difference of arrival (TDOA) measurements in the presence of random noises in both the TDOA and sensor location measurements. We develop an improved unscented Kalman filter (UKF), where the mobile model is augmented by incorporating the sensor positions into the state vector and the number of sigma points is enlarged in the improved unscented transformation. Although the proposed improved UKF method requires higher computational complexity, its estimation performance is improved in comparison with that of the classical UKF method which ignores the sensor position uncertainties. Simulations are used to demonstrate the good performance of the proposed method.