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Robust extended Kalman filtering for nonlinear systems with multiplicative noises

机译：具有乘法噪声的非线性系统的鲁棒扩展卡尔曼滤波

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摘要

In this paper, we investigate the robust filter design problem for nonlinear systems with multiplicative noises. The aim of the problem is to design a state estimator with a predictor-corrector structure, such that the upper bound on the state estimation error variance is minimized. A robust extended Kalman filter (REKF) is proposed based on a novel method to obtain the upper bound on the variances of the multiplicative noises. Further analysis shows that the proposed filter guarantees a bounded energy gain from the multiplicative noises to the estimation error. The REKF is implemented on the satellite attitude determination system that consists of the gyroscopes and the star sensors. Its performance is illustrated by using the real data obtained from a gyroscope. Simulation results show that the REKF outperforms another robust algorithm.
机译：在本文中，我们研究了具有乘法噪声的非线性系统的鲁棒滤波器设计问题。该问题的目的是设计具有预测器-校正器结构的状态估计器，以使状态估计误差方差的上限最小。基于一种新颖的方法，提出了一种鲁棒的扩展卡尔曼滤波器（REKF），以获得乘性噪声方差的上限。进一步的分析表明，所提出的滤波器保证了从乘性噪声到估计误差的有限能量增益。 REKF在由陀螺仪和恒星传感器组成的卫星姿态确定系统上实现。通过使用从陀螺仪获得的真实数据来说明其性能。仿真结果表明，REKF优于另一种鲁棒算法。

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