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首页> 外文期刊>Transactions of the Japan society for aeronautical and space sciences >Sequential Multiple Model Filtering with Interrupted Measurements
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Sequential Multiple Model Filtering with Interrupted Measurements

机译：带有中断测量的顺序多模型过滤

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摘要

This paper considers the joint detection and filtering problem of discrete-time stochastic systems when the measurements are interrupted in a random fashion. By formulating the measurement interruptions into two-state Markov chains, a sequential multiple model filter is developed from the Bayesian point of view. With a soft switching mechanism, the proposed filter automatically abandons the useless measurements in the interrupted time intervals, and captures the correct measurements for recursive estimation. Compared with the widely used interacting multiple model filter, the new filter has a more simple structure and requires less time for computation. A numerical example shows that the proposed multiple model filter can effectively solve the target tracking problem with interrupted range measurements.
机译：当测量以随机方式中断时，本文考虑了离散随机系统的联合检测和滤波问题。通过将测量中断表述为两个状态的马尔可夫链，从贝叶斯的观点出发，开发了一个顺序多模型滤波器。利用软切换机制，所提出的滤波器会在中断的时间间隔内自动放弃无用的测量，并捕获正确的测量以进行递归估计。与广泛使用的交互多模型过滤器相比，新的过滤器具有更简单的结构并且需要更少的计算时间。数值算例表明，所提出的多模型滤波器可以有效地解决目标跟踪问题，并具有测距中断的特点。

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