Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data associa...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2019-11, Vol.67 (22), p.5896-5911
Main Authors: Sharma, Pranay, Saucan, Augustin-Alexandru, Bucci, Donald J., Varshney, Pramod K.
Format: Article
Language:English
Subjects:
Algorithms
Belief Propagation
Clutter
Current measurement
Gaussian distribution
Gibbs Sampling
Localization
Measurement uncertainty
Multi-target tracking
Multiple target tracking
Self-localization
Sensors
Target tracking
Time measurement
Uncertainty
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Summary:Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data association uncertainty, i.e., the associations between measurements and target tracks are unknown. Existing CS and tracking algorithms either make the assumption of no data association uncertainty or employ a hard-decision rule for measurement-to-target associations. We propose a novel decentralized SCS-MTT method for an unknown and time-varying number of targets under association uncertainty. Marginal posterior densities for agents and targets are obtained by an efficient belief propagation (BP) based scheme while data association is handled by marginalizing over all target-to-measurement association probabilities. Decentralized single Gaussian and Gaussian mixture implementations are provided based on average consensus schemes, which require communication only with one-hop neighbors. An additional novelty is a decentralized Gibbs mechanism for efficient evaluation of the product of Gaussian mixtures. Numerical experiments show the improved CS and MTT performance compared to the conventional approach of separate localization and target tracking.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2946017