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Constrained state estimation using noisy destination information

机译：使用嘈杂的目的地信息进行约束状态估计

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摘要

Tracking performance can be improved by the incorporation of a destination constraint. However, directly using available noise-corrupted destination information in the existing destination constrained filtering methods may lead to performance degradation. To address this limitation, in this paper, the true destination is also treated as a state to be estimated along with the target state. Then, the destination constraint information is leveraged by using the relationship between the state components and a newly constructed pseudo-measurement. An uncertain destination constrained augmented state filter (UDC-ASF) with its state being augmented by destination, in which the unscented Kalman filter (UKF) is used to deal with the strong nonlinearity of the measurements, is proposed to produce both constrained target state and destination estimates. Moreover, the unknown slope and the intercept of the straight-line representing the destination constraint can also be augmented into the state vector and two pseudo-measurements are constructed in the process. The corresponding UDC-ASF with slope and intercept (UDC-ASF-Sl) is derived. The a priori noisy destination is used to initialize the two proposed filters. An extension of the UDC-ASF to track multiple targets heading to the same destination is also discussed. Simulation results demonstrate the effectiveness of the proposed methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：可以通过合并目标约束来提高跟踪性能。但是，在现有的目标受限过滤方法中直接使用可用的，受噪声破坏的目标信息可能会导致性能下降。为了解决此限制，在本文中，将真实目的地也与目标状态一起视为要估计的状态。然后，通过使用状态分量和新构建的伪度量之间的关系来利用目标约束信息。提出了一种不确定的目标约束增强状态滤波器（UDC-ASF），其状态被目标增强，其中使用无味卡尔曼滤波器（UKF）来处理测量的强非线性，从而生成约束目标状态和目标状态。目的地估算值。此外，未知斜率和代表目标约束的直线的截距也可以增加到状态向量中，并且在此过程中构造了两个伪度量。导出具有斜率和截距的对应的UDC-ASF（UDC-ASF-S1）。先验噪声目的地用于初始化两个建议的过滤器。还讨论了UDC-ASF的扩展，以跟踪前往同一目的地的多个目标。仿真结果证明了所提方法的有效性。 （C）2019 Elsevier B.V.保留所有权利。

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