A fixed-lag smoothing solution to out-of-sequence information fusion problems
Subhash Challa, Robin J. Evans, Xuezhi Wang, Jonathan Legg · Communications in Information and Systems · 2002
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. Introduction.In a multi-sensor centralized tracking system, sensors produce observations that are sent to a fusion center over communication networks which can introduce random delays.Thus there is no guarantee that data are received in the order they have originated.This problem has appeared in the literature under various names such as the Out Of Sequence Measurements (OOSM) problem [1,2,3,4], the problem of tracking with random sampling and delays [5], [6], [7], and the problem of incorporating random time delayed measurements [8].Most sensor networks communicate tracks (processed measurements) rather than raw measurements, owing to the prior existence of embedded trackers, and prohibitive communication bandwidths that favor summaries of measurements, rather than the measurements themselves.As in the case of a centralized tracking system, random delays are introduced resulting in reception of tracks out of sequence.This paper addresses this out-of-sequence tracking problem with equivalent measurements.Equivalent measurements, their use and methods of extraction from track estimates are discussed extensively by Blackman and Popoli [4], Frenkel [9] and Drum- *