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首页> 外文期刊>Control Theory & Applications, IET >Unscented Kalman filtering for target tracking systems with packet dropout compensation
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Unscented Kalman filtering for target tracking systems with packet dropout compensation

机译：具有包丢失补偿的目标跟踪系统的无味卡尔曼滤波

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摘要

The unscented Kalman filtering problem is investigated for a class of non-linear discrete-time stochastic systems subject to random packet dropout. Here, a random variable, which obeys Bernoulli distribution with known conditional probability, is introduced to depict the phenomenon of packet dropout occurring in a stochastic way. Based on the compensation technology of packet dropout, the one-step prediction value of observer instead of zero-input is used as a compensator when packet dropout occurs. Owing to taking the phenomenon of random packet dropout into account, the authors need to compute parameters to reduce the effects of the compensator. Then, based on the minimum mean square error principle, a new unscented Kalman filtering algorithm is proposed such that, for the random packet dropout, the filtering error is minimised. By solving the recursive matrix equations, the filter gain matrices and error covariance matrices can be obtained and the proposed results can be easily verified by using the standard numerical software. They finally provide two examples, one is a numerical example to show the performance of the proposed approach, and the other is to solve the problem of target estimation for a tracking system with random packet dropout.
机译：针对一类随机分组丢失的非线性离散时间随机系统，研究了无味卡尔曼滤波问题。这里，引入一个随机变量，该随机变量以已知的条件概率服从伯努利分布，以描述以随机方式发生的数据包丢失的现象。基于数据包丢失的补偿技术，当发生数据包丢失时，将观察者的一步预测值而不是零输入用作补偿器。由于考虑了随机分组丢失的现象，因此作者需要计算参数以减小补偿器的影响。然后，基于最小均方误差原理，提出了一种新的无味卡尔曼滤波算法，使得对于随机分组丢失，滤波误差最小。通过求解递归矩阵方程，可以获得滤波器增益矩阵和误差协方差矩阵，并且可以使用标准数值软件轻松验证所提出的结果。他们最终提供了两个示例，一个是通过数值示例来说明所提出方法的性能，另一个是解决具有随机数据包丢失的跟踪系统的目标估计问题。

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