NDLI: Track association using augmented state estimates

Content Provider	IEEE Xplore Digital Library
Author	Chee-Yee Chong Mori, S.
Copyright Year	2015
Description	Author affiliation: Syst. & Technol. Res., Sunnyvale, CA, USA (Mori, S.)
Abstract	Track association has not received as much attention as track fusion in distributed multi-sensor multitarget tracking, especially for targets whose motion models involve process noise. One exception is an association metric that uses the cross-covariance of the track state estimates at a single time. For track fusion, it has been shown that the centralized state estimate can be obtained by fusion of augmented state estimates consisting of state estimates at multiple times. Association using augmented state estimates is even more natural because the association likelihood should consider the entire state trajectory of a track, and not just the estimates at the last time. Starting with a general association likelihood function, we show that augmented states allow exact evaluation of the track association likelihood. For problems involving Gaussian densities, the association metric is the standard Mahalanobis or chi-square metric with the single time state estimate replaced by the augmented state estimate. Simulations compare the performance of association using augmented state estimates of different lengths and the method using cross-covariances. Results demonstrate excellent performance for augmented state association even when the full augmented state is not used and filtered estimates instead of smoothed estimates are used.
Starting Page	854
Ending Page	861
File Size	917575
Page Count	8
File Format	PDF
e-ISBN	9780982443866
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ISIF
Subject Keyword	Target tracking Noise Mathematical model Noise measurement Measurement uncertainty track fusion association likelihood augmented state estimates cross-covariance association non-zero process noise track association
Content Type	Text
Resource Type	Article

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Comparison of augmented state track fusion methods for non-full-rate communication

Simulations studies of multisensor track association and fusion methods

Estimation covariance of measurement fusion on track-to-track problem

Track-to-track association with augmented state

Comparison of track fusion rules and track association metrics

Heterogeneous track-to-track fusion

Robust Kalman track fusion in target tracking with uncertainties

Influence of the Sensor Local Track Covariance on the Track-to-Track Sensor Fusion

Track association and fusion using Janossy measure density functions

Track association using augmented state estimates

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