Sigma-Point Kalman Filters for GPS Based Position Estimation
Tang Bo, Cui Pingyuan, Chen Yangzhou · 2006
Stand-alone GPS based position estimation problem using GPS raw data, pseudo-range and Doppler shifts measurements are the concept of fusing noisy observations. A family of improved derivative nonlinear Kalman filters called sigma point Kalman filter (SPKF) are applied to a nonlinear model of GPS based position estimation in this paper. Simulations are made to compare the filter with the traditional iterative least square (ILS) method and extended Kalman filter (EKF) method, results indicate that under same conditions, SPKF has higher filtering accuracy and more stable estimation performance