Kalman filtering with state equality constraints
Kalman filtering with state equality constraints
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DOI：
10.1109/7.993234
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发表时间：
2002-01-01
影响因子：
4.4
通讯作者：
Chia, TL
中科院分区：
文献类型：
--
作者：
Simon, D;Chia, TL
卡尔曼过滤器通常用于估计动态系统的状态。但是，在使用Kalman过滤时，通常有已知的模型或信号信息被忽略或使用启发式信息。例如，对状态值的约束（可能基于物理考虑）通常被忽略，因为它们不容易适合Kalman滤波器的结构。这里开发了一种在卡尔曼过滤器中纳入状态平等约束的严格分析方法。限制可能是时间变化。在每个时间步骤中，无约束的卡尔曼滤波器解决方案都会投射到状态约束表面上。这显着提高了过滤器的预测准确性。在简单的非线性车辆跟踪问题上证明了该算法的使用。
Kalman filters are commonly used to estimate the states of a dynamic system. However, in the application of Kalman filters there is often known model or signal information that is either Ignored or dealt with heuristically. For instance, constraints on state values (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. A rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed here. The constraints may be time varying. At each time step the unconstrained Kalman filter solution Is projected onto the state constraint surface. This significantly improves the prediction accuracy of the filter. The use of this algorithm is demonstrated on a simple nonlinear vehicle tracking problem.