Mobile localization via high-degree cubature Kalman filter with sensor position uncertainties

This paper investigates the passive localization of a mobile source based on time difference of arrival (TDOA) measurements when the sensor positions suffer from random uncertainties. In the formulation of the dynamic system, the nonlinear measurement function contains random parameters, so the classical high-degree cubature Kalman filtering (CKF) method is unrealizable. We develop an augmented high-degree CKF method to deal with the random parameters, where the system is augmented by incorporating the random sensor positions into the state vector and the number of cubature points is enlarged. Although the proposed augmented high-degree CKF method requires more computational complexity, its estimation accuracy is improved in comparison with that of the classical high-degree CKF method which ignores the sensor position uncertainties. Monte Carlo simulations are used to illustrate the good performance of the proposed method.