A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks Article Swipe
Xinbin Li
,
Chenglin Zhang
,
Lei Yan
,
Song Han
,
Xinping Guan
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s18010008
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s18010008
Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.
Related Topics
Concepts
Particle filter
Wireless sensor network
Computer science
Node (physics)
Underwater
Filter (signal processing)
Underwater acoustic communication
Support vector machine
Noise (video)
Algorithm
Artificial intelligence
Engineering
Computer vision
Image (mathematics)
Computer network
Geology
Oceanography
Structural engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s18010008
- https://www.mdpi.com/1424-8220/18/1/8/pdf?version=1513871871
- OA Status
- gold
- Cited By
- 39
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2778859783
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2778859783Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s18010008Digital Object Identifier
- Title
-
A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-12-21Full publication date if available
- Authors
-
Xinbin Li, Chenglin Zhang, Lei Yan, Song Han, Xinping GuanList of authors in order
- Landing page
-
https://doi.org/10.3390/s18010008Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/18/1/8/pdf?version=1513871871Direct 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/1424-8220/18/1/8/pdf?version=1513871871Direct OA link when available
- Concepts
-
Particle filter, Wireless sensor network, Computer science, Node (physics), Underwater, Filter (signal processing), Underwater acoustic communication, Support vector machine, Noise (video), Algorithm, Artificial intelligence, Engineering, Computer vision, Image (mathematics), Computer network, Geology, Oceanography, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
39Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 1, 2022: 2, 2021: 8Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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