Abstract
In this article, three algorithms are implemented, i.e., least-square estimator (LSE), Kalman filter (KF), extended Kalman filter (EKF), and one algorithm is proposed designated as cross-correntropy Kalman filter (CCKF) for precise GNSS/GPS applications and GAGAN-based aircraft landings over the low-latitude Indian subcontinent. The proposed method uses cross-correntropy (CC) as an optimal criterion, a local similarity measure, unlike minimum mean square error. Also, it uses an iterative approach called fixed point for renovating the rearward estimates. The proposed algorithm is compared with the several navigational algorithms (LSE, KF, and EKF), which are implemented and presented in this paper. The theoretical study and simulation results indicate that the proposed GPS navigation algorithm’s efficiency based on the criterion of CC outperforms that of the general approaches (LSE, KF, and EKF).
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Sirish Kumar, P., Srilatha Indira Dutt, V.B.S., Suman, J.V., Krishna Rao, P. (2022). Ambiguities in GNSS Receiver Position Estimation: A Comparative Statistical Error Characterization. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_15
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DOI: https://doi.org/10.1007/978-981-16-2543-5_15
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