Multiple Model Adaptive Rank Estimation for Integrated Navigation During Mars Entry

Xiao, Qiang; Fu, Huimin; Wang, Zhihua; Zhang, Yongbo

doi:10.1017/s037346331600059x

Cited by 4 publications

(1 citation statement)

References 31 publications

(61 reference statements)

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“…However, the prior knowledge of the uncertainties should be known before the use of DEKF. Considering the uncertain system parameters associated with the three degree‐of‐freedom (3‐DOF) dynamic model and the measurement systematic errors, the authors in References 13‐15 have proposed a multi‐model adaptive rank estimation (MMARE) filter of radio beacons/IMU integrated navigation system. However, it was assumed that the practical atmosphere density is among the pre‐assumed values, and such a type of method might encounter numerical underflows.…”

Section: Introductionmentioning

confidence: 99%

Mars entry navigation under biases based on adaptive Huber divided difference filter

Xia

Zhu

Cui

et al. 2022

Adaptive Control & Signal

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Summary To address the problems of unknown disturbances and measurement outliers at Mars entry phase, three integrated navigation schemes are proposed in this study by complying with divided difference filters, that is, integrated navigation schemes based on the Huber first‐order divided difference filter (HDDF1), adaptive Huber first‐order divided difference filter (AHDDF1), and adaptive Huber second‐order divided difference filter (AHDDF2). The novel versions of predictive state estimation error covariance (PSEEC) and measurement noise covariance (MNC) are deduced by adopting the ρ function of the Huber case to modify the cost function of the standard Kalman filter. To be specific, the HDDF1 is derived by embedding the novel PSEEC and MNC in the general first‐order divided difference filter. To more specifically enhance the performance of the filter under significant biases, an adaptive forgetting factor is introduced in the HDDF1. On that basis, the AHDDF1 is derived. Likewise, the AHDDF2 is yielded. The numerical simulations are presented to verify the effectiveness of the proposed navigation schemes.

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Section: Introductionmentioning

confidence: 99%