A Novel Robust Algorithm for Passive Location and Tracking with Angles and Time Difference of Arrival Measurements

In order to realize fast, stable and unbiased location and tracking, a new robust location and tracking algorithm with angles and time difference of arrival (TDOA) measurements is proposed. The proposed algorithm introduces the observation error correlation matrix into the constraint condition and uses constraint least squares minimization on the pseudo linear equation which includes quadratic constraint about the state vector and the asymptotically unbiased state estimate can be got by taking a generalized eigen-decomposition to a pair of matrix pencil. Simulation results indicate that the proposed algorithm has more stable performance and higher precision compared with EKF algorithm and LS algorithm and its location error can reach the Cramer-Rao bound. Its advantage will be more obvious when the measurement errors are large or the two stations’s measurement errors are different, thus it has strong practicability.