The DOI matches its Crossref record.
Spatial distribution model for tracking extended objects
Abstract
A Bayesian filter has been developed for tracking an extended object in clutter based on two simple axioms: (i) the numbers of received target and clutter measurements in a frame are Poisson distributed (so several measurements may originate from the target) and (ii) target extent is modelled by a spatial probability distribution and each target-related measurement is an independent ‘random draw’ from this spatial distribution (convolved with a sensor model). Diffuse spatial models of target extent are of particular interest. This model is especially suitable for a particle filter implementation, and examples are presented for a Gaussian mixture model and for a uniform stick target convolved with a Gaussian error. A rather restrictive special case that admits a solution in the form of a multiple hypothesis Kalman filter is also discussed and demonstrated.
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
Cited by
Tracking of Extended Objects and Group Targets Using Random Matrices
Feldmann, M., Franken, D., & Koch, W. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2101064 · Cited by 481
Poisson models for extended target and group tracking
Gilholm, K., Godsill, S., Maskell, S., & Salmond, D. (2005) · SPIE Proceedings · doi:10.1117/12.618730 · Cited by 278
Self-citationExtended Object Tracking with Random Hypersurface Models
Baum, M., & Hanebeck, U. D. (2014) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2013.120107 · Cited by 272
Models and Algorithms for Tracking of Maneuvering Objects Using Variable Rate Particle Filters
Godsill, S. J., Vermaak, J., Ng, W., & Li, J. F. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.894708 · Cited by 81
Metadata
- Authors
- Gilholm, K., & Salmond, D.
- Venue
- IEE Proceedings - Radar, Sonar and Navigation
- Published
- 2005-10-07
- Volume
- 152
- Issue
- 5
- Pages
- 364-371
- ISSN
- 1350-2395 (ISSN-L)
- 1359-7086
- OpenAlex
- W2091365726
- Cited by
- 307
- References
- 0
- FWCI
- 6.87
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Spatial distribution model for tracking extended objects. https://app.cochat.ai/papers/10.1049/ip-rsn:20045114
- The paper
- Gilholm, K., & Salmond, D. (2005). Spatial distribution model for tracking extended objects. IEE Proceedings - Radar, Sonar and Navigation. https://doi.org/10.1049/ip-rsn%3A20045114
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
