Sequential Monte Carlo methods for multitarget filtering with random finite sets

By B. Vo, Sumeetpal S. Singh, Arnaud Doucet. IEEE Transactions on Aerospace and Electronic Systems, 2005

1,280 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/5c87bbb3024feadb624a2866508096618f32936c

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Citations in the corpus

Extended Object Tracking: Introduction, Overview and Applications (2016, neutral): “, [126], [192].”
The Labeled Multi-Bernoulli Filter (2014, background): “Gaussian mixture and particle implementations of these filters have been developed in [10], [12]–[16].”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “Amongst these we identify particularly particle methods for random finite set models, see [122], and particle methods for continuous time diffusion models ([123] provides the basic theory for this development in the batch (MCMC) setting).”
The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations (2009, methodology): “Sequential Monte Carlo (SMC) implementations [6]–[8] with provable convergence [6], [9], [10] and closed form solutions [11], [12] have opened the door to numerous novel extensions and applications which can be found in the surveys [3], [13].”
Clustering for point pattern data (2017, background): “The Poisson model is completely determined by the intensity function u = λpf [36], [37].”
The Gaussian Mixture Probability Hypothesis Density Filter (2006, background): “Index Terms— Intensity function, multiple-target tracking, optimal ﬁltering, point processes, random sets.”
Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013, background): “Implementations of these filters are developed in [23]–[26], [22], with convergence analysis given in [23], [27]–[30].”
Multisensor data fusion: A review of the state-of-the-art (2013, methodology): “As a result two approximation methods, namely, Gaussian Mixture (GM) and Sequential Monte Carlo (SMC), have been used in the literature to further ease the implementation stage for these filters [89,90].”
A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, background): “3While the FISST densities are not probability densities, they have been shown to be equivalent to probability densities relative to some reference measure [13].”
Bayesian approach to extended object and cluster tracking using random matrices (2008, methodology): “It seems worth noting that Ron Mahler’s approach [19, 20, 26, 25] has been described as a method for tracking a large and unknown number of possibly closely spaced targets.”
Adaptive Target Birth Intensity for PHD and CPHD Filters (2012, methodology): “The described limitation affects both methods of PHD/CPHD filter implementation: the sequential Monte Carlo (SMC) method [14, 15] and the finite Gaussian mixtures (GM) [10, 13].”
A Random-Finite-Set Approach to Bayesian SLAM (2011, positive): “Mahler’s finite-set statistics (FISST) provide practical mathematical tools to deal with RFSs [16], [17], which is based on a notion of integration and density that is consistent with point-process theory [18].”
Model-Based Multiple Instance Learning (2017, positive): “For this choice of reference measure, it was shown in [31] that the integral of f is given by ∫”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “In this paper we use Mahler’s Finite Set Statistics (FISST) notion of integration/density (which is consistent with measure theoretic integration/density [38]) to characterize RFSs.”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “In this subsection we use the PHD/CPHD update and prediction [8], [29], [50]–[52] to look-ahead and identify the prediction/update components that generate significant updated/predicted components.”
Joint Detection and Estimation of Multiple Objects From Image Observations (2010, background): “Mahler’s Finit e Set Statistics (FISST) provides powerful yet practical mat hematical tools for dealing with RFSs [10], [11], based on a notion of integration and density that is consistent with po int process theory [22].”
Model-based classification and novelty detection for point pattern data (2016, background): “The Poisson model is completely determined by the intensity function u = λpf [12],[21], [22].”
PHD filters of higher order in target number (2006, background): “Instances are Erdinc, Willett, and Bar-Shalom [16], and Vo, Singh, and Doucet [49].”