Abstract
In the presented algorithm, the Gaussian maximum a posteriori (MAP) filter and the traditional extended Kalman filter (EKF) are implemented in parallel to obtain the adaptive ability. One of the elemental filters, the EKF yields high precision in the scenario with low process noise, whereas the other elemental filter, the Gaussian MAP filter is adopted to utilize the measurement maximally in the presence of high process noise. The state estimates of the parallel filters are combined automatically based on the confidence for the underlying situation, such that the presented algorithm can adapt to different operation scenarios. The presented algorithm can provide precise relative attitude and position knowledge between two spacecrafts. It is applicable for many space missions, such as spacecraft formation flying, autonomous rendezvous docking and failed satellite removal. This is the first paper that presents the adaptive MAP estimator based on parallel multiple filters for spacecraft relative navigation.
This study was supported in part by China Natural Science Foundation (61573059) and Beijing Natural Science Foundation (4162070).
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Xiong, K., Zhang, H. (2018). Adaptive Maximum a Posteriori Filtering for Relative Attitude and Position Estimation. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-6499-9_13
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DOI: https://doi.org/10.1007/978-981-10-6499-9_13
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