Robust Multitarget Tracking in Interference Environments: A Message-Passing Approach
Xianglong Bai, Hua Lan, Zengfu Wang, Quan Pan, Yuhang Hao, and Can Li

TL;DR
This paper introduces a robust multitarget tracking algorithm using message-passing that simultaneously estimates target and clutter states, effectively handling nonuniform, time-varying clutter environments.
Contribution
It proposes a novel message-passing based method that jointly estimates clutter and target states, incorporating signal strength features and hybrid data association for improved robustness.
Findings
Outperforms PHD and CPHD filters in simulations
Effectively handles nonuniform, time-varying clutter
Improves target discrimination accuracy
Abstract
Multitarget tracking in the interference environments suffers from the nonuniform, unknown and time-varying clutter, resulting in dramatic performance deterioration. We address this challenge by proposing a robust multitarget tracking algorithm, which estimates the states of clutter and targets simultaneously by the message-passing (MP) approach. We define the non-homogeneous clutter with a finite mixture model containing a uniform component and multiple nonuniform components. The measured signal strength is utilized to estimate the mean signal-to-noise ratio (SNR) of targets and the mean clutter-to-noise ratio (CNR) of clutter, which are then used as additional feature information of targets and clutter to improve the performance of discrimination of targets from clutter. We also present a hybrid data association which can reason over correspondence between targets, clutter, and…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Radar Systems and Signal Processing · Advanced Adaptive Filtering Techniques
