A HIGH PRECISION MLAT METHOD COMBINING M-ESTIMATION AND EXTENDED KALMAN FILTER IN AIRPORT SURFACE SURVEILLANCE

The positioning accuracy of traditional multilateration method(MLAT)in airport surface surveillance is easily affected by the observation error in NLOS environment.To solve this problem,a high-precision MLAT method combining M-estimation and extended Kalman filter(EKF)is proposed.The TDOA data measured by the surface receiving station was constructed into a numerical model.Using the idea of Huber-M estimation,the observation updating step in standard EKF was changed to a weighted least square linear regression problem,so as to improve the anti-interference ability of EKF to non-Gaussian observation noise.The improved EKF was applied to location estimation.The simulation results show that the proposed method is robust to the observation noise of TDOA and achieves high positioning accuracy.