Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 12; pp. 23630 - 23649
• Main Authors: Chen, Xiyuan, Wang, Xiying, Xu, Yuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.12.2014; MDPI
• Subjects: Dynamical systems; Errors; Extended Kalman filter; Global Positioning System; Global Positioning System (GPS); Global positioning systems; GPS; iterated extended Kalman filter (IEKF); Longitude; model error; Noise; Nonlinear dynamics; nonlinear filtering; Performance evaluation; Receivers; Satellites; Sensors; Statistics; vector-tracking; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s141223630

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.