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.
翻译:通常比较方便的做法是将国家估算任务分为较小的“当地”任务,即每个当地估计者估计整个系统状态的一个子集。然而,忽视国家估算之间的交叉变量术语可能导致过于自信的估算,最终会降低估计值的准确性。常见的级联过滤技术侧重于当当地估计者共用一个共同的国家矢量时的建模跨变量问题。本信引入了一种新颖的级联和分散过滤方法,当当地估计者考虑不同的状态矢量时,该方法与交叉变量相近。提议的估算值在模拟和三维态度和定位估计问题的实验中被验证。拟议方法与一种忽略跨变量术语、以西格玛点为基础的共变量截分过滤器和全州过滤器的天相比,同时与全州过滤器相对。