Multiclass Generalized Labeled Multi-Bernoulli Filter With Kernel Density Estimation for Target Tracking in Unknown Backgrounds

In multitarget tracking (MTT), the prior information of background parameters, such as clutter intensity and detection probability, exerts a significant influence on the performance of the filter. Thus, when performing MTT in unknown backgrounds, the filter should possess the capability to estimate...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 25; no. 15; pp. 29075 - 29090
Main Authors: Wu, Qinchen, Sun, Jinping, Yang, Bin, Li, Juan, Wang, Yanping
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clutter
Estimation
Filtering algorithms
Generalized labeled multi-Bernoulli (GLMB) filter
Graphical models
Information filters
Kernel
kernel density estimation (KDE)
Multiple target tracking
multitarget tracking (MTT)
Noise measurement
nonuniform clutter
Parameters
Radio frequency
random finite sets (RFSs)
robust filtering
Target tracking
Tracking
unknown clutter rate
unknown detection probability
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:In multitarget tracking (MTT), the prior information of background parameters, such as clutter intensity and detection probability, exerts a significant influence on the performance of the filter. Thus, when performing MTT in unknown backgrounds, the filter should possess the capability to estimate background parameters. To achieve the online estimation of the unknown background parameters, this article introduces a multiclass generalized labeled multi-Bernoulli filter based on the kernel density estimation (KDE-MC-GLMB). The proposed filter uses MC-GLMB to model the clutter generators and targets of interest, respectively. Via propagating the joint density forward in time, the filter can jointly estimate the clutter intensity and multitarget states. To adapt to the unknown clutter spatial density, a KDE-based clutter estimator is incorporated into the MC-GLMB recursion, thereby enabling accurate estimation of the clutter spatial density. In addition, to enhance computational efficiency, we implement L-scan truncation on the number of Gaussian kernels in KDE and introduce a clutter density updating strategy based on the best association map (BAM) to limit the repetition of KDE processing. As for the unknown detection probability, the Beta-Gaussian mixture implementation of the proposed filter is provided in this article as well. The simulation results demonstrate that, in the unknown background environment, the KDE-MC-GLMB filter exhibits superior tracking performance compared to existing random-finite-set-based robust filters.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3581056