Sequential Multiple Model Filtering with Interrupted Measurements

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概要

• 論文の詳細を見る

• 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.

• 社団法人 日本航空宇宙学会の論文
• 2007-05-04

著者

• Zhang Yong-an

  School Of Astronautics Beihang University

• Zhou Di

  Center For Control And Guidance Harbin Institute Of Technology

• Duan Guang-ren

  Center For Control And Guidance Harbin Institute Of Technology

• Zhou D

  Center For Control And Guidance Harbin Institute Of Technology

関連論文

• Sequential Multiple Model Filtering with Interrupted Measurements
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