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首页> 外文期刊>Aerospace science and technology >Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter
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Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter

机译：基于最大似然原理和移动视野估计的自适应无味卡尔曼滤波器

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摘要

AbstractThe classical unscented Kalman filter (UKF) requires prior knowledge on statistical characteristics of system noises for state estimation of a nonlinear dynamic system. If the statistical characteristics of system noises are unknown or inaccurate, the UKF solution will be deteriorated or even divergent. This paper presents a novel adaptive UKF by combining the maximum likelihood principle (MLP) and moving horizon estimation (MHE) to overcome this limitation. This method constructs an optimization based estimation of system noise statistics according to MLP. Subsequently, it further establishes a moving horizon strategy to improve the computational efficiency of the MLP based optimization estimation. Based on above, a new expectation maximization technique is developed to iteratively compute the MLP and MHE based noise statistic estimation by replacing complex smoothed estimates with filtering estimates for further improvement of the computational efficiency. The proposed method can achieve the online estimation of system noise statistic and enhance the robustness of the classical UKF. The efficacy of the proposed adaptive UKF is demonstrated through simulations and practical experiments in the INS/GPS integrated navigation.
机译： 摘要 经典的无味卡尔曼滤波器（UKF）需要有关系统噪声统计特性的先验知识，以便进行非线性动态系统的状态估计。如果系统噪声的统计特征未知或不准确，则UKF解决方案将变差甚至分散。本文提出了一种新的自适应UKF，它通过结合最大似然原理（MLP）和移动视界估计（MHE）来克服这一局限性。该方法根据MLP构建了基于优化的系统噪声统计估计。随后，它进一步建立了移动视野策略，以提高基于MLP的优化估计的计算效率。基于上述，开发了一种新的期望最大化技术，通过用滤波估计替换复杂的平滑估计来迭代地计算基于MLP和MHE的噪声统计估计，以进一步提高计算效率。所提方法可以实现系统噪声统计量的在线估计，提高经典UKF的鲁棒性。通过在INS / GPS组合导航中的仿真和实际实验证明了所提出的自适应UKF的有效性。

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