Resolving Ambiguity Using Fusion Classification for Ultra-short Baseline Positioning Systems

WANG Yan; LI Qing; FU Jin; LIANG Guolong

doi:10.11999/JEIT160825

Volume 39 Issue 6

Jun. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(6): 1348-1354

WANG Yan, LI Qing, FU Jin, LIANG Guolong. Resolving Ambiguity Using Fusion Classification for Ultra-short Baseline Positioning Systems[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1348-1354. doi: 10.11999/JEIT160825

Citation:

WANG Yan, LI Qing, FU Jin, LIANG Guolong. Resolving Ambiguity Using Fusion Classification for Ultra-short Baseline Positioning Systems[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1348-1354. doi: 10.11999/JEIT160825

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Resolving Ambiguity Using Fusion Classification for Ultra-short Baseline Positioning Systems

doi: 10.11999/JEIT160825

Funds:

The National Natural Science Foundation of China (51279043, 11504064, 61405041), The Postdoctoral Scientific Research Foundation of Heilongjiang Province (LBH-Q15025), The Technical Basic Research Project (JSJL2016604B003), The Science Foundation for the Returned Overseas Scholars of Heilongjiang Province, China (JJ2016LX0051)

Received Date: 2016-08-03
Rev Recd Date: 2017-02-08
Publish Date: 2017-06-19

Abstract

Abstract

To solve the phase-difference ambiguity problem in Ultra-Short BaseLine (USBL) underwater acoustic positioning systems, an ambiguity resolution and localization method based on Multiple Classifier Fusion (MCF) is proposed. Firstly, the multiple classifier system is built. Then, ambiguity resolution problem is formulated as classifying and recognizing the ambiguity integer, and Choquet integral is utilized for fusing the results of multiple classifiers. Finally, the category of ambiguity integer is obtained and the target is located. The unambiguous observation condition of the target position is derived. Without constructing an inter-sensor spacing less than half the wavelength, unambiguous aperture of the array is effectively enlarged. Moreover, as statistical characteristics of the observation data are fully utilized, the positioning accuracy approaches the Cramer-Rao bound. Simulation results verify the effectiveness of the proposed method.
- Ultra-Short BaseLine (USBL) positioning,
- Phase-difference ambiguity,
- Multiple Classifier Fusion (MCF),
- Fuzzy integral

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References(29)

References

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