Globally optimal flight path update with adding or removing out-of-sequence measurements

  • Authors:

  • Xiaojing Shen;Yingting Luo;Yunmin Zhu;Enbin Song;Zhisheng You

  • Affiliations:

  • College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, PR China and College of Computer Science, Sichuan University, Chengdu, Sichuan, 610064, PR China;College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, PR China;College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, PR China;College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064, PR China;College of Computer Science, Sichuan University, Chengdu, Sichuan, 610064, PR China

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2010

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Abstract

In a multisensor target tracking system, observations produced by sensors can arrive at a central processor out of sequence. There have been some state estimate update algorithms for out-of-sequence measurements (OOSMs). In this paper, we propose a flight path update algorithm for a sequence with arbitrary delayed OOSMs. The new algorithm has three advantages: (1) it is a globally optimal recursive algorithm; (2) it is an algorithm for arbitrary delayed OOSMs including the case of interlaced OOSMs with less storage, compared with the optimal state update algorithm in Zhang, Li, and Zhu (2005); (3) it can update the current whole flight path in addition to the current single state with less computation, i.e., the dimension of the matrices which need to be inverted is not more than that of the state in the process of updating the past @? (lag steps) estimates and corresponding error covariances. Besides, this algorithm can be easily modified to derive a globally optimal flight path update with removing an earlier (incorrectly associated) measurement.