A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation Article Swipe
In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA(2) metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements.
Related Topics
Concepts
Algorithm
Gibbs sampling
Bernoulli's principle
Computer science
Filter (signal processing)
Belief propagation
Metric (unit)
Tracking (education)
Recursion (computer science)
Pruning
Artificial intelligence
Bayesian probability
Computer vision
Aerospace engineering
Economics
Decoding methods
Pedagogy
Psychology
Biology
Operations management
Agronomy
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14174209
- https://www.mdpi.com/2072-4292/14/17/4209/pdf?version=1661516512
- OA Status
- gold
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293660975
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4293660975Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14174209Digital Object Identifier
- Title
-
A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief PropagationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-26Full publication date if available
- Authors
-
Chenghu Cao, Yongbo ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14174209Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/17/4209/pdf?version=1661516512Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/17/4209/pdf?version=1661516512Direct OA link when available
- Concepts
-
Algorithm, Gibbs sampling, Bernoulli's principle, Computer science, Filter (signal processing), Belief propagation, Metric (unit), Tracking (education), Recursion (computer science), Pruning, Artificial intelligence, Bayesian probability, Computer vision, Aerospace engineering, Economics, Decoding methods, Pedagogy, Psychology, Biology, Operations management, Agronomy, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2022-08-26 |
| publication_year | 2022 |
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