The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
The Gaussian Mixture Probability Hypothesis Density Filter
Abstract
A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first-order statistic of the random finite set of targets, in time. At present, there is no closed-form solution to the PHD recursion. This paper shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.
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Cites
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Julier, S. J., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,588
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Cited by
PHD filters of higher order in target number
Mahler, R. (2007) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2007.4407475 · Cited by 1,020
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
Vo, B.-T., Vo, B.-N., & Cantoni, A. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2007924 · Cited by 851
Self-citationAnalytic Implementations of the Cardinalized Probability Hypothesis Density Filter
Vo, B.-T., Vo, B.-N., & Cantoni, A. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.894241 · Cited by 824
Self-citationDeep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations
Arasaratnam, I., Haykin, S., & Hurd, T. R. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2056923 · Cited by 580
Metadata
- Authors
- Vo, B.-N., & Ma, W.-K.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2006-11
- Volume
- 54
- Issue
- 11
- Pages
- 4091-4104
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2137585588
- Cited by
- 1,919
- References
- 39
- FWCI
- 34.44
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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