Proceedings ArticleDOI
A Particle PHD Filter for Multi-Sensor Multi-Target Tracking Based on Sequential Fusion
Fanbin Meng,Yanling Hao,Quanxi Xia,Taishan OuYang,Wei Zou +4 more
- pp 1-5
TLDR
This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking, showing the advantage of the fusion tracking over the single radar multi- target tracking.Abstract:
This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking. The tracking system involves potentially nonlinear target dynamics described by Markov state space model and nonlinear measurements. Each sensor reports measurements to the tracking system, which performs sequential estimation of the current state using the particle PHD filter, which propagates only the first order statistical moment of the full posterior of the multi-target state. Simulation results are also given and compared with a single radar multi-target tracking, showing the advantage of the fusion tracking over the single radar multi-target tracking. Keywordsrandom finite sets; particle PHD filter; multitarget tracking; sequential fusionread more
Citations
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Proceedings Article
Multi-target tracking for measurement models with additive contributions
TL;DR: A novel moment-based multi-target filter, the Additive Likelihood Moment (ALM) filter, where the measurements are affected by all targets, and the algorithm has a lower estimation error than MCMC particle methods while achieving 80% savings in terms of computational time.
Journal ArticleDOI
Distributed multi-sensor multi-view fusion based on generalized covariance intersection
TL;DR: An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed that decomposes each posterior PHD into well-separated components (clusters).
Journal ArticleDOI
Asynchronous multi-rate multi-sensor fusion based on random finite set
TL;DR: Three algorithms are proposed for asynchronous sensor fusion problem for an arbitrary number of sensors with different sampling rates in the framework of the random finite set (RFS) theory, including a centralized fusion algorithm and two distributed fusion algorithms.
Proceedings Article
Multi target tracking with CPHD filter based on asynchronous sensors
TL;DR: A method based on Cardinalized Probability Hypothesis Density (CPHD) filter improved with probability of target type is presented and the tuning of filter parameter depending of sensor is explained and the particular case of non detection due to occlusion with camera is developed.
Journal Article
A Survey of PHD Filter Based Multi-target Tracking
TL;DR: An overview of the emergence, the development and the present research situation of the Probability Hypotensity (PHD) filter in target tracking is presented in this article, where special attention is paid to the following areas: PHD filter, its implementation method, the peak and track extraction technology, multi-sensor multitarget tracking, multiuser management, PHD smoother, the assessment metrics of multi-target tracking performance, and also the relevant applications.
References
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Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
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TL;DR: Recursion Bayes filter equations for the probability hypothesis density are derived that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets and it is shown that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.
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Proceedings Article
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TL;DR: This paper outlines the inconsistencies of existing metrics in the context of multi- object miss-distances for performance evaluation, and proposes a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.
Proceedings ArticleDOI
Sequential monte carlo implementation of the phd filter for multi-target tracking
TL;DR: In this article, a probabilistic hypothesis density (PHD) filter is proposed to estimate the PHD by a set of weighted random samples which are propa- gated over time using a generalized SMC method.