Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

David Cormack; James R. Hopgood

doi:10.1109/TAES.2020.3046090

Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

David Cormack, James R. Hopgood

School of Engineering & Physical Sciences

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

21 Downloads (Pure)

Abstract

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multi-modal, multi-sensor target tracking. In this Multiple Target Tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle Belief Propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another.We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a Random Finite Set (RFS) approach, namely the Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

Original language	English
Pages (from-to)	1524-1537
Number of pages	14
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	57
Issue number	3
Early online date	5 Jan 2021
DOIs	https://doi.org/10.1109/TAES.2020.3046090
Publication status	Published - Jun 2021

Keywords

Belief propagation (BP)
PHD filter
camera
message passing
multiple target tracking (MTT)
radar
sensor fusion
sensor registration

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2020.3046090

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abstract = "Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multi-modal, multi-sensor target tracking. In this Multiple Target Tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle Belief Propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another.We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a Random Finite Set (RFS) approach, namely the Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.",

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Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

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Keywords

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