The Correntropy Kalman Filter: A Robust Estimator for GPS Applications

India suffers from low satellite visibility. If any GPS receiver is operated in urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. Among all these factors, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel kinematic positioning algorithm is proposed designated as the Correntropy Kalman Filter (CKF) that adopts the robust correntropy criterion as the optimality criterion instead of using the well-known minimum mean square error (MMSE). A novel fixed-point algorithm is then used to update the posterior estimates. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. The proposed algorithm results are then compared with the Least Square Estimator (LSE), traditional Kalman Filter (KF), and Extended Kalman Filter (EKF) algorithms. Results prove that the proposed CKF algorithm exhibits significant improvement in position estimation compared to the other three recursive algorithms (i.e., LSE, KF, and EKF). And Statistical position Accuracy Measures (SAM) like DRMS, CEP, SEP, etc. are additionally used for performance evaluation of the proposed algorithm.