Abstract
To improve the performance of the cubature kalman filter (CKF) in nonlinear non-Gaussian filtering system, the Gaussian sum cubature kalman filter (GSCKF) is proposed. A formulation of the CKF for the nonlinear system with non-Gaussian process noise and non-Gaussian measurement noise is presented. The GSCKF uses the Gaussian sum theory to divide the non-Gaussian system into several Gaussian sub-systems. And each subsystem is conducted the filtering process by the CKF. A target tracking problem with non-Gaussian noise is used as a simulation application to compare the filtering performance of the GSCKF. The simulation results show that the GSCKF outperforms the unscented Kalman filter (UKF) and CKF under non-Gaussian conditions. It proves that for the nonlinear systems with non-Gaussian noise, the filtering performance of the GSCKF is significantly improved.
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Acknowledgement
This work was supported in part by the National Defense Basic Scientific Research Program of China under grant No. JCKY2019606D001), in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant KYCX19_0300.
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Qian, C., Chen, Q., Song, C., Ji, C., Pan, H. (2022). Algorithm of Gaussian Sum Based Cubature Kalman Filter for Non-Gaussian Systems. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_40
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DOI: https://doi.org/10.1007/978-981-16-6372-7_40
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