The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations
Open accessAbstract
It is shown analytically that the multitarget multiBernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multiBernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Sequential Monte Carlo Methods in Practice
Arnaud Doucet, B. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,516
Stochastic Geometry and its Applications
Chiu, S. N., Stoyan, D., Kendall, W. S., & Mecke, J. (2013) · Wiley series in probability and statistics · doi:10.1002/9781118658222 · Cited by 4,410
An Introduction to the Theory of Point Processes
Daley, D. J., & Vere-Jones, D. (2007) · Probability and its applications · doi:10.1007/978-0-387-49835-5 · Cited by 3,437
Bar‐Shalom, Y. (1988) · Medical Entomology and Zoology · Cited by 3,157
Bar‐Shalom, Y., Fortmann, T. E., & Cable, P. G. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.398863 · Cited by 3,088
Multitarget bayes filtering via first-order multitarget moments
Mahler, R. (2003) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2003.1261119 · Cited by 2,274
Statistical Multisource-Multitarget Information Fusion
(2007) · Cited by 2,046
The Gaussian Mixture Probability Hypothesis Density Filter
Vo, B., & Ma, W. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881190 · Cited by 1,946
Self-citationA Consistent Metric for Performance Evaluation of Multi-Object Filters
Schuhmacher, D., Vo, B.-T., & Vo, B. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.920469 · Cited by 1,901
Self-citationSequential monte carlo methods for multi-target filtering with random finite sets
Vo, B., Singh, S. S., & Boucet, A. (2005) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2005.1561884 · Cited by 1,238
Self-citationMultiple-Target Tracking with Radar Applications
Blackman, S. S. (1986) · Medical Entomology and Zoology · Cited by 1,180
Stochastic Geometry and Its Applications.
Stoyan, D., Kendall, W. S., & Mecke, J. (1989) · Biometrics · doi:10.2307/2531521 · Cited by 1,143
Multiple Target Tracking with Radar Applications
Cooper, D. C. (1987) · Electronics and Power · doi:10.1049/ep.1987.0251 · Cited by 1,096
PHD filters of higher order in target number
(2007) · doi:10.1109/taes.2007.4441756 · Cited by 990
Analytic 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-citationComparison of resampling schemes for particle filtering
Douc, R., & Cappé, O. (2005) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2005.195385 · Cited by 822
An introduction to the theory of point processes
Rota, G. (1989) · Advances in Mathematics · doi:10.1016/0001-8708(89)90024-8 · Cited by 761
On performance evaluation of multi-object filters
Schuhmacher, D., Vo, B.-T., & Vo, B. (2008) · UWA Profiles and Research Repository (University of Western Australia) · Cited by 726
Self-citationGoodman, I. R., Mahler, R., & Nguyen, H. T. (1997) · Springer eBooks · doi:10.1007/978-94-015-8929-1 · Cited by 598
Multi-target particle filtering for the probability hypothesis density
Sidenbladh, H. (2003) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2003.177321 · Cited by 235
Particle-systems implementation of the PHD multitarget-tracking filter
Zajic, T. R., & Mahler, R. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.488533 · Cited by 141
Comparison of Resampling Schemes for Particle FilteringPreprint
Douc, R., Cappé, O., & Moulines, É. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0507025 · Cited by 127
Particle Filters — A Theoretical Perspective
Crisan, D. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9_2 · Cited by 125
Convergence results for the particle PHD filter
Clark, D. E., & Bell, J. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.874845 · Cited by 116
Approximate multisensor CPHD and PHD filters
Mahler, R. (2010) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2010.5711984 · Cited by 99
Random finite sets in Multi-object filtering
Vo, B.-T. (2008) · UWA Profiles and Research Repository (University of Western Australia) · Cited by 97
Self-citationConvergence of the SMC Implementation of the PHD Filte
Johansen, A. M., Singh, S. S., Doucet, A., et al. (2006) · Methodology And Computing In Applied Probability · doi:10.1007/s11009-006-8552-y · Cited by 55
An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker
Panta, K., Ba-Ngu-Vo, & Clark, D. E. (2006) · International Conference on Intelligent Sensing and Information Processing · doi:10.1109/icisip.2006.4286102 · Cited by 40
A survey of PHD filter and CPHD filter implementations
Maher, R. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.721125 · Cited by 38
On multi-Bernoulli approximations to the Bayes multi-target filter
Vo, B., & Cantoni, A. (2007) · UWA Profiles and Research Repository (UWA) · Cited by 14
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- ulmke, 2007, gaussian mixture cardinalized probability hypothesis density filter for ground moving target tracking, Proc Int Conf Information Fusion
Cited by
Sorted by citations
Soldi, G., Gaglione, D., Forti, N., Millefiori, L. M., Braca, P., Carniel, S., Simone, A. D., Iodice, A., Riccio, D., Daffina, F. C., et al. (2021) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2021.3070884 · Cited by 44
“Existing MTT algorithms can be broadly classified as “vectortype” algorithms, such as the joint probabilistic data association (JPDA) filter [36] and the multiple hypothesis tracker (MHT) [40], [41], and “set-type” algorithms, such as the (cardinalized) probability hypothesis density filter [42]–[45] and multi-Bernoul…”
Method
Metadata
- Authors
- Vo, B.-T., Vo, B.-N., & Cantoni, A.
- Affiliations
- The University of Melbourne (AU)
- The University of Western Australia (AU)
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2009-02
- Volume
- 57
- Issue
- 2
- Pages
- 409-423
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2110354007
- Cited by
- 851
- References
- 30
- Citations by year
- 2026: 42 · 2025: 48 · 2024: 50 · 2023: 58 · 2022: 58 · 2021: 53 · 2020: 68 · 2019: 71 · 2018: 82 · 2017: 57 · 2016: 69 · 2015: 72 · 2014: 37 · 2013: 35 · 2012: 21
- FWCI
- 15.83
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- eSpace (Curtin University) (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Funders
- Australian Research Council (DP0878158)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- multi-target tracking, sequential Monte Carlo, linear Gaussian models, unscented transform, nonlinear models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
