Communications in Information and Systems

Volume 2 (2002)

Number 4

A fixed-lag smoothing solution to out-of-sequence information fusion problems

Pages: 325 – 348

DOI: http://dx.doi.org/10.4310/CIS.2002.v2.n4.a1

Authors

Subhash Challa (Center for Sensor Signal and Information Processing, Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, Australia)

Robin J. Evans (Center for Sensor Signal and Information Processing, Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, Australia)

Xuezhi Wang (Center for Sensor Signal and Information Processing, Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, Australia)

Jonathan Legg (Center for Sensor Signal and Information Processing, Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, Australia)

Abstract

Multi-sensor tracking using delayed, out-of-sequence Information (OOSI) is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that information (in the form of raw or processed measurements) is received out-of-time-order at the fusion center. Owing to compatibility with legacy sensors and limited communication bandwidth most practical fusion systems send track information rather than raw measurements to the fusion node. This paper presents a unified Bayesian approach to handling this out-of-sequence information problem and provides implementable sub-optimal algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements/tracks. Such an approach leads to a solution involving the joint probability density of current and past target states. A fixed-lag smoothing framework, developed by John Moore and his students almost 30 years ago, forms the basis of our algorithm. Under linear Gaussian assumptions, the Bayesian solution reduces to an Augmented State Kalman Filter (AS-KF). Computationally efficient versions of the AS-KF are considered in this paper. Simulations are presented to evaluate the performance of these solutions.

Keywords

target tracking, networked sensors, time delayed measurements, out-of-sequence measurements (OOSM), out-of-sequence tracks (OOST), out-of-sequence information (OOSI), fixed-lag smoothing, smoothing

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