On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems

Yan WANG; Qing LI; Guangpu ZHANG

doi:10.11999/JEIT180056

Volume 40 Issue 11

Oct. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2578-2583

Yan WANG, Qing LI, Guangpu ZHANG. On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2578-2583. doi: 10.11999/JEIT180056

Citation:

Yan WANG, Qing LI, Guangpu ZHANG. On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2578-2583. doi: 10.11999/JEIT180056

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On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems

doi: 10.11999/JEIT180056

1.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61405041), The National Key Research and Development Program (2017YFC0306900), The Technology Basic Research Program (JSJL2016604B003), The Qingdao National Laboratory for Marine Science and Technology Open Fund (QNLM2016ORP0102)

Received Date: 2018-01-16
Rev Recd Date: 2018-07-09

Available Online: 2018-07-18

Publish Date: 2018-11-01

Abstract

Abstract

Complicated underwater environment puts forward high requirements on the fault-tolerant and reliability of underwater acoustic localization systems. An anti-outlier localization method based on K-Means Clustering and Decision Fusion (KMCDF) is proposed for integrated Long baseline/Ultra-Short BaseLine (L/USBL) systems. Firstly, the target position is preliminarily estimated by the multi-parameter redundant information measured by the integrated system. Then, the clustering degree of the preliminary coordinates is analyzed by k-means clustering. According to the incompatibility between outliers and normal parameters, the outliers are identified by the decision fusion method. Furthermore, the impact of outliers on positioning is eliminated. Simulation analysis shows that the proposed method fully incorporates the multi-parameter information, and the tolerance of outliers is better than the existing anti-outlier positioning methods based on the time-delay parameter. Lake trial results demonstrate further the effectiveness of the proposed method.
- Reliable localization,
- Integrated baseline,
- Outlier,
- Clustering,
- Decision fusion

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References(16)

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