Multitarget Bayes filtering via first-order multitarget moments

By Ronald P. S. Mahler. 2003

2,271 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5efa73676159b7d32de7cc8d1cd94bf8aec8345c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Rao-Blackwellized particle filter for multiple target tracking (2007, methodology): “However, this probability model of extension to an unknown number of targets is closer to the approach presented in [9] than to the approach in [15] despite the more strict Bayesian nature of the latter.”
A phd Filter for Tracking Multiple Extended Targets Using Random Matrices (2012, methodology): “The Bayesian framework used conveniently supplies probabilistic uncertainty measures to describe the extension estimates.”
Tracking of extended objects and group targets using random matrices — a new approach (2008, background): “In particular, the probability hypothesis density (PHD) filter [3]–[5] has been suggested as one possibility to track an unknown number of possibly closely spaced targets while simultaneously solving the association problem in an approximate yet very efficient manner, especially in its Gaussian mixt”
PHD filters of higher order in target number (2006, methodology): “We have likewise found it useful to introduce transform methods into multitarget analysis [22].”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “The theorem confirms what one would intuitively expect: that the prediction of the PPP component (37) follows the standard PHD prediction step of [1] [2], and that the multi- Bernoulli tracks are predicted independently in an equivalent manner to the MeMBer [5] [6] (excluding birth of new targets,…”
Consensus CPHD Filter for Distributed Multitarget Tracking (2013, methodology): “cluster processes is at the basis of the PHD [16] or, respectively, Cardinalized PHD (CPHD) [17] filtering approaches to MTT.”
Joint Detection and Estimation of Multiple Objects From Image Observations (2010, background): “B.-N. Vo and B.-T. Vo are with the School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia (e-mail: ba-ngu.vo@uwa.edu.au; ba-tuong.vo@uwa.edu.au).”
Message Passing Algorithms for Scalable Multitarget Tracking (2018, positive): “The intensity function (also known as PHD) of that Poisson RFS is chosen such that the RFS Kullback–Leibler divergence relative to f (  k | Z 1:k−1 ) is minimized [106].”
Multi-Sensor Fusion in Automated Driving: A Survey (2020, methodology): “Mahler puts forward a probability hypothesis density (PHD) method based on RFS [162], [163].”