Journal ArticleDOI
Fast Gaussian Mixture Probability Hypothesis Density Filter
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TLDR
In this article, a fast Gaussian mixture probability hypothesis density (GMPHD) algorithm is proposed based on gating strategy, which can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.Abstract:
Although the Gaussian mixture probability hypothesis density (GMPHD) filter is a multi-target tracker that can alleviate the computational intractability of the optimal multi-target Bayes filter and its computational complex is lower than that of sequential Monte Carlo probability hypothesis density (SMCPHD), its computational burden can be reduced further. In the standard GMPHD filter, each observation should be matched with each component when the PHD is updated. In practice, time cost of evaluating many unlikely measurements-to-components parings is wasteful, because their contribution is very limited. As a result, a substantial reduction in complexity could be obtained by directly setting relative value associated with these parings. A fast GMPHD algorithm is proposed in the paper based on gating strategy. Simulation results show that the fast GMPHD can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.read more
Citations
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Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking
TL;DR: An efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior.
Journal Article
Distributed Tracking with a PHD Filter using Efficient Measurement Encoding.
Biruk K. Habtemariam,Ampikathasan Aravinthan,Ratnasingham Tharmarasa,Kumaradevan Punithakumar,T. Lang,Thia Kirubarajan +5 more
TL;DR: Authors’ addresses: B. Habtemariam, A. Aravinthan, R. Tharmarasa, and T. Kirubarajan, McMaster University, Hamilton, ON, Canada.
References
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Book
Statistical Multisource-Multitarget Information Fusion
TL;DR: This comprehensive resource provides an in-depth understanding of finite-set statistics (FISST) - a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm.
Journal ArticleDOI
The Gaussian Mixture Probability Hypothesis Density Filter
Ba-Ngu Vo,Wing-Kin Ma +1 more
TL;DR: Under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture and closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posteriorintensity are derived.
Journal ArticleDOI
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
TL;DR: The proposed CPHD implementations not only sidestep the need to perform data association found in traditional methods, but also dramatically improve the accuracy of individual state estimates as well as the variance of the estimated number of targets when compared to the standard PHD filter.
Journal ArticleDOI
Adaptive Target Birth Intensity for PHD and CPHD Filters
TL;DR: A new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets is presented, which enables us to adaptively design the target birth intensity at each scan using the received measurements.
Journal ArticleDOI
Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter
TL;DR: In this article, a Gaussian mixture probability hypothesis density (GM-PHD) recursion is proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets.