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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Hierarchical adaptive Kalman filtering for interplanetary orbitdetermination
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Hierarchical adaptive Kalman filtering for interplanetary orbitdetermination

机译：行星际轨道确定的分层自适应卡尔曼滤波

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摘要

A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters. The unknown or uncertain parameters can include elements of the state transition matrix, observation mapping matrix, process noise covariance matrix, and measurement noise covariance matrix. The gating network performs on-line adaptation of the weights given to individual filters based on performance. The mixture-of-experts approach is extended here to a hierarchical architecture which involves multiple levels of gating. The proposed architecture provides a multilevel hypothesis testing capability. The utility of the hierarchical architecture is illustrated via the problem of interplanetary navigation (Mars Pathfinder) using simulated radiometric data. It serves as a useful tool for assisting navigation teams in the process of selecting the parameters of the navigational filter over various operating regimes. It is shown that the scheme has the capability of detecting changes in the system parameters and switching filters appropriately for optimal performance. Furthermore, the expectation-maximization (EM) algorithm is shown to be applicable in the proposed framework
机译：最近，采用由门控网络调节的专家混合框架，引入了一种模块化，灵活的自适应卡尔曼滤波方法。每个专家都是用未知系统参数的不同实现建模的卡尔曼滤波器。未知或不确定的参数可以包括状态转换矩阵，观察映射矩阵，过程噪声协方差矩阵和测量噪声协方差矩阵的元素。选通网络根据性能对分配给各个滤波器的权重进行在线调整。专家混合方法在此处扩展为涉及多层选通的分层体系结构。所提出的体系结构提供了多层次的假设测试能力。通过使用模拟辐射数据的行星际导航（Mars Pathfinder）问题说明了层次结构的实用性。它是一种有用的工具，可帮助导航团队在各种操作方式下选择导航过滤器的参数。结果表明，该方案具有检测系统参数变化并适当切换滤波器以实现最佳性能的能力。此外，期望最大化（EM）算法被证明适用于所提出的框架

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