State-Transition-Algorithm-Based Underwater Multiple Objects Localization With Gravitational Field and Its Gradient Tensor

Recently, several techniques using gravitational data such as gravitational field and its gradient tensor have been developed to localize underwater multiple objects. However, performances of these existed techniques largely rely on proper selections of the initial values and, thus, are likely trapp...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 17; číslo 2; s. 192 - 196
Hlavní autoři: Zhao, Tingting, Tang, Jingtian, Hu, Shuanggui, Lu, Guangyin, Zhou, Xiaojun, Zhong, Yiyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustics
Algorithms
Dynamic stability
Electromagnetics
Geologic measurements
Global optimization
Gravitation
gravitational data
Gravitational fields
Gravity
Gravity field
Heuristic algorithms
Localization
Mathematical analysis
multiple objects localization
Optimization
Performance assessment
Software upgrading
state transition algorithm (STA)
State-space methods
Tensors
Underwater
ISSN:1545-598X, 1558-0571
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Popis
Shrnutí:Recently, several techniques using gravitational data such as gravitational field and its gradient tensor have been developed to localize underwater multiple objects. However, performances of these existed techniques largely rely on proper selections of the initial values and, thus, are likely trapped into local minima. To deal with this issue, a global optimization algorithm, named as the state transition algorithm (STA), is investigated to localize multiple objects with both gravitational field and gravitational gradient tensor data sets in this letter. Using a heuristic random search strategy, the proposed algorithm features good global search capability and avoids the dependence of using proper initial values. To assess the performance of the proposed method, different models which contain three and four underwater objects are tested. The experimental results demonstrate that the proposed method is promising with sound stability and strong antinoise ability for dynamic localization problem with multiple objects.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2917784