On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems
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摘要: 复杂的水下环境对水声定位系统的容错性及可靠性提出较高要求，对于长基线/超短基线组合水声定位系统，该文提出基于k-means聚类和决策融合的抗异常参量定位方法(KMCDF)。该方法首先通过组合定位系统测量的多参量冗余信息对目标位置进行初步测量，再利用k-means聚类对初测值的聚集度进行分析，根据异常参量与正常参量间的不相容性，采用决策融合方法对异常参量进行识别，进而消除异常值对定位结果的影响。仿真分析表明，与现有的基于时延参量的抗异常值方法相比，提出的抗异常值定位方法充分融合了多参量观测信息，对异常值的容忍度较高。湖试结果进一步验证了该方法的有效性。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.
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Key words:
- Reliable localization /
- Integrated baseline /
- Outlier /
- Clustering /
- Decision fusion
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