Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update

  • Sirish Kumar Pagoti Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India
  • Srilatha Indira Dutt Vemuri Gandhi Institute of Technology and Management (Deemed to be University), Visakhapatnam, Andhra Pradesh, India
  • Mohammad Khaja Mohiddin Bhilai Institute of Technology, Raipur, Chhattisgarh, India
Keywords: correntropy criterion (CC), correntropy Kalman filter (CKF), fixed-point algorithm, global positioning system (GPS), minimum mean square error (MMSE)


The accuracy of position estimation plays a key role in many of the precise positioning applications such as category I (CAT-I) aircraft landings, survey work, etc. To improve the accuracy of position estimation, a novel kinematic positioning algorithm designated as correntropy Kalman filter (CKF) is proposed in this study. Instead of minimum mean square error (MMSE), correntropy criterion (CC) is used as the optimality criterion of CKF. The prior estimates of the state and covariance matrix are computed in CKF and a novel fixed-point algorithm is then used to update the posterior estimates. The data of a dual-frequency global positioning system (GPS) receiver located at Indian Institute of Science (IISc), Bangalore (13.021°N/77.5°E) is collected from Scripps Orbit and Permanent Array Centre (SOPAC) to implement the proposed algorithm. The results of the proposed CKF algorithm are promising and exhibit significant improvement in position estimation compared to the conventional methods.


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How to Cite
S. K. Pagoti, S. I. D. Vemuri, and M. K. Mohiddin, “Enhanced Kalman Filter Navigation Algorithm Based on Correntropy and Fixed-Point Update”, Int. j. eng. technol. innov., Nov. 2021.