使用Sigma点变换的级联滤波（扩展版）
Cascaded Filtering Using the Sigma Point Transformation (Extended Version)
摘要：
将状态估计任务分成较小的“局部”任务通常是方便的，其中每个局部估计器估计整个系统状态的子集。然而，忽略状态估计之间的互协方差项会导致过度自信的估计，这最终会降低估计器的精度。常见的级联滤波技术集中在局部估计器共享公共状态向量时的互协方差建模问题。这封信介绍了一种新的级联和分散的滤波方法，近似的互协方差时，当地估计考虑不同的状态向量。在三维姿态和位置估计问题的仿真和实验中，所提出的估计进行了验证。所提出的方法相比，一个天真的级联滤波的方法，忽略了互协方差项，基于西格玛点的协方差交叉滤波器，和一个完整的状态滤波器。在仿真和实验中，所提出的滤波器优于朴素和协方差交叉滤波器，而执行比较全状态滤波器。
英文摘要：
It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.
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