GPS/SINS的STUPF滤波算法研究

Jizhuang Zhao, Longhua Ma, Ming Xu, Feng Liu, Shaohui Huang
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摘要

考虑到滤波器发散问题,提出了一种结合STF (Strong Tracting Unscented Particle filter)、UKF (Unscented Kalman filter)和UPF (Unscented Particle filter)[1]的STUPF (Strong Tracting Unscented Particle filter)算法。该算法通过引入衰落因子K来调整滤波器的增益,降低旧数据的权重,提高新数据的权重,从而提高UPF滤波器的跟踪性能。传统的重采样算法解决了退化问题,容易造成粒子耗竭;扩展粒子滤波算法EKF对突变状态的跟踪能力较弱,但粒子损耗较小;强跟踪粒子滤波算法STF可以提高突变状态的跟踪能力,但不能改善粒子的退化。本文提出的STUPF算法很好地解决了这些问题,仿真结果也验证了STUPF算法的有效性。
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Research of STUPF filter algorithm on GPS/SINS
Considering the filter divergence problem, a STUPF (Strong Tracting Unscented Particle Filter) algorithm which combines STF (Strong Tracting Filter), UKF (Unscented Kalman Filter) and UPF (Unscented Particle Filter)[1] is proposed. The STUPF algorithm improves the tracking performance of UPF filter by introducing the fading factor K to adjust the filter gain to reduce the weight of the old data, improve the weight of the new data. The traditional re-sampling algorithm can solve the problem of degradation, it is easy to cause the particle depletion; Despite less particle depletion, the extended particle filter algorithm EKF is very weak to track the mutation state; Strong tracking particle filter algorithm STF can improve the tracking ability of the mutation state, but it can't improve the particle degradation. In this paper, the STUPF algorithm is a good solution to solve these problems, and the simulation results also verify the effectiveness of the STUPF algorithm.
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