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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >State Estimation With Trajectory Shape Constraints Using Pseudomeasurements
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State Estimation With Trajectory Shape Constraints Using Pseudomeasurements

机译：使用伪测量的具有轨迹形状约束的状态估计

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摘要

In this paper, a trajectory shape constraint on target motion is investigated, and the corresponding state estimation method is presented. This type of constraint occurs when only the shape of the target trajectory is known a priori, without any other necessary information to exactly describe the specific trajectory. For example, one may only know that the target moves along a straight line. This prior knowledge of trajectory can be considered as a constraint to improve tracking performance. To describe the shape constraint arising from the straight line assumption, the state vector is augmented by the states at previous time steps. This facilitates the description of the shape constraint using the components of the state vector. Pseudomeasurements are then constructed based on these relationships to incorporate the constraint into an estimation process to improve the performance. The redundancy of the complete set of pseudomeasurements for a given augmented state vector is analyzed, and the minimal set of pseudomeasurements, which describes the constraint exactly, is proposed. The time evolution equation for the augmented state and the measurement equation using the minimal pseudomeasurement set are formulated. A trajectory shape constraint Kalman filter (TSCKF) is then proposed for simultaneous filtering and smoothing. Since both the measurement vector and the state vector are high dimensional, the cubature Kalman filter is used in the proposed TSCKF to deal with the strong nonlinearity in this problem. Monte Carlo simulations illustrate the effectiveness of the proposed TSCKF and the improvement in both filtering and smoothing accuracies by incorporating the trajectory shape constraint.
机译：研究了目标运动轨迹形状约束，提出了相应的状态估计方法。当仅先验知道目标轨迹的形状而没有任何其他必要信息来准确描述特定轨迹时，就会发生这种类型的约束。例如，人们可能只知道目标沿着一条直线移动。可以将这种轨迹的先验知识视为提高跟踪性能的约束。为了描述由直线假设引起的形状约束，状态向量被先前时间步长的状态所扩充。这有助于使用状态向量的分量来描述形状约束。然后根据这些关系构造伪测量，以将约束条件合并到估计过程中以提高性能。分析了给定扩展状态向量的完整伪测量集的冗余，并提出了精确描述约束的最小伪测量集。提出了增强状态的时间演化方程和使用最小伪测量集的测量方程。然后提出了一种轨迹形状约束卡尔曼滤波器（TSCKF），用于同时滤波和平滑。由于测量向量和状态向量都是高维的，因此在拟议的TSCKF中使用库尔曼卡尔曼滤波器来解决该问题中的强非线性问题。蒙特卡洛仿真通过结合轨迹形状约束说明了所提出的TSCKF的有效性以及滤波和平滑精度的改进。

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