A Bayesian solution and its approximations to out-of-sequence measurement problems

作者: Subhash Challa , Robin J. Evans , Xuezhi Wang

DOI: 10.1016/S1566-2535(03)00037-X

关键词: Joint probability distributionFusion centerJoint Probabilistic Data Association FilterSmoothingGaussianMathematical optimizationClutterSequenceComputer scienceKalman filter

摘要: Abstract Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it often the case that are received out-of-time-order at fusion center. This paper presents Bayesian solution this and provides approximate, implementable algorithms for both cluttered non-cluttered scenarios involving single multiple time-delayed measurements. Such approach leads joint probability density current past target states. contrast, existing solutions in literature modify sensor measurement equation account time delay explicitly deal with resulting correlations arise process noise state. proposed paper, cross treated implicitly. Under linear Gaussian assumptions, reduces augmented state Kalman filter (AS-KF) devoid clutter probabilistic data association (AS-PDA) clutter. Computationally efficient versions AS-KF AS-PDA considered paper. Simulations presented evaluate performance these solutions.

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