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Resampling-free Stochastic Integration Filter

机译：免重采样随机积分滤波器

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The paper deals with the state estimation of nonlinear stochastic systems with additive Gaussian noises by means of the Gaussian filters leveraging numerical integration rules. The filters were derived under the assumption of the joint state and measurement predictive density being Gaussian, which is violated by the system nonlinearity. Such violation can hardly be monitored by the standard Gaussian filters, which re-generate a new set of points for each involved numerical integration to accommodate their variance increase due to the additive noises. The paper proposes a stochastic integration filter algorithm that modifies the points instead of their resampling and thus admits reusing the points in the next time steps. The distribution of the points can thus bear more information than just the first two moments in case of the standard Gaussian filters. The acquired information is then utilized for the Gaussian assumption monitoring purposes. In the event of the assumption violation, the filter may change its behavior. As a by-product of reusing the points, the computational costs of the proposed filter are significantly reduced compared to the standard stochastic integration filter.
机译：通过利用数值积分规则的高斯滤波器，研究了具有加性高斯噪声的非线性随机系统的状态估计。滤波器是在关节状态和测量预测密度为高斯的假设下得出的，这被系统非线性所破坏。标准高斯滤波器几乎无法监视这种冲突，标准高斯滤波器会为每个涉及的数值积分重新生成一组新的点，以适应由于加性噪声而导致的方差增加。本文提出了一种随机积分滤波算法，该算法修改点而不是重新采样，从而允许在后续步骤中重复使用这些点。因此，在使用标准高斯滤波器的情况下，这些点的分布不仅可以承受前两个矩的信息，还可以承载更多的信息。然后将获取的信息用于高斯假设监视目的。在违反假设的情况下，过滤器可能会更改其行为。作为重复使用这些点的副产品，与标准随机积分滤波器相比，该滤波器的计算成本大大降低。

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