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Multi-feature Combination Track-to-track Association Based on Histogram Statistics Feature
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摘要: 现有的航迹关联方法主要有基于统计和基于模糊数学两大类方法。基于统计的方法大多依赖阈值的设置，基于模糊数学的方法参数设置复杂，且多数方法相关比较时只考虑单个航迹点的信息。针对现有问题，该文首先从航迹的整体出发，在传统欧式距离度量的基础上，提出了一种距离分布直方图的特征并提取了航迹的相似特征，有效地利用了航迹间的整体特性，具有较好的抗噪声性能以及关联准确率。其次充分考虑了船舶运动特征以及不同数据源位置精度，提取了航迹间的速度差分布直方图特征、传感器来源特征。然后将这些特征组合并利用机器学习的方法训练关联模型，有效地避免了需要人工设定阈值以及参数设置复杂的问题。最后，该文构建了一个真实的船舶数据集，实验结果表明距离分布直方图特征相比传统的距离特征总体关联准确率提高了3.23%～11.65%，组合特征相较于单一的距离分布直方图特征总体关联准确率提高了0.068%，验证了该文方法的有效性。Abstract: Existing track-to-track association methods are mainly based on statistics and fuzzy mathematics. However, most methods based on statistics depend on thresholds, and parameters based on fuzzy mathematics are complex to set. In addition, most methods only consider the information of a single track point in comparison. To solve the existing problems, this paper presents a distance distribution histogram feature to extract the similarity features of a trajectory and measure them using the standardized Euclidean distances; this method effectively utilizes the characteristics of the whole trajectory and has a good anti-noise performance and accuracy. The motion features of ships and the location accuracy of different data sources were fully considered. After obtaining the histogram features of velocity difference and the source features of sensors, the authors combined them and trained association models using machine learning, which effectively avoids the problem of manually setting thresholds and complex parameter settings. Finally, a real ship data set was constructed. The experimental results show that compared with the traditional distance feature, the overall association accuracy was improved by 3.23%～11.65% using the distance distribution histogram feature, and by 0.068% using the combination feature, which verifies the effectiveness of the proposed method.
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图 1 距离分布直方图特征计算示意图
Figure 1. Schematic diagram of feature calculation of distance distribution histogram
图 5 两条航迹对应点时间不同示意图
Figure 5. Different timings of the corresponding points on the two tracks
图 9 传统距离特征无法识别而距离分布直方图能够识别的目标
Figure 9. The targets that can’t be identified by traditional distance feature but can be identified by distance distribution histogram
图 10 距离分布直方图特征关联失败的目标
Figure 10. The targets of distance distribution histogram feature error association
图 12 单一特征错误关联组合特征正确识别情况
Figure 12. Correct identification of single feature miscorrelation combination features
表 1 平均距离特征阈值方法与机器学习方法指标对比
Table 1. Comparison of average distance characteristics threshold method and machine learning method index
阈值 精度 查准率 查全率 F1 机器学习 – 0.9478 0.9617 0.9590 0.9603 精度最高阈值 4524.7 0.8906 0.9170 0.9170 0.9170 平均数阈值 76231.5 0.5998 1.0000 0.3927 0.5640 中位数阈值 8878.8 0.8142 0.9948 0.7218 0.8366 
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表 2 加权距离特征阈值方法与机器学习方法指标对比
Table 2. Comparison of weighted distance characteristics threshold method and machine learning method index
阈值 精度 查准率 查全率 F1 机器学习 – 0.9264 0.9538 0.9335 0.9435 精度最高阈值 0.0003 0.8626 0.9822 0.8062 0.8856 平均数阈值 0.2681 0.4528 1.0000 0.1679 0.2902 中位数阈值 0.0008 0.8080 0.9858 0.7190 0.8335
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表 3 最大距离特征阈值方法与机器学习方法指标对比
Table 3. Comparison of maximum distance characteristics threshold method and machine learning method index
阈值 精度 查准率 查全率 F1 机器学习 – 0.8636 0.8904 0.9043 0.8973 精度最高阈值 14024.4 0.8036 0.9377 0.7520 0.8346 平均数阈值 108357.6 0.6094 0.9696 0.4026 0.5866 中位数阈值 20853.2 0.7713 0.9471 0.6917 0.7995
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表 4 不同特征指标对比
Table 4. Comparison of different characteristics
特征 精度 查准率 查全率 F1 AUC 距离分布直方图 0.9801 0.9826 0.9873 0.9849 0.9768 平均距离 0.9478 0.9617 0.9590 0.9390 0.9426 加权距离 0.9264 0.9538 0.9335 0.9435 0.9373 最大距离 0.8636 0.8904 0.9043 0.8973 0.8446
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表 5 组合特征指标对比
Table 5. Comparison of composite features
组合特征 精度 查准率 查全率 F1 AUC DDH 0.9801 0.9858 0.9840 0.9849 0.9783 DDH+DTW 0.9829 0.9859 0.9882 0.9870 0.9804 DDH+DTW+SDDH 0.9842 0.9873 0.9887 0.9880 0.9820 DDH+DTW+SDDH+数据来源特征 0.9869 0.9901 0.9901 0.9901 0.9855
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表 6 不同机器学习方法指标对比
Table 6. Comparison of different machine learning indicators
学习方法 精度 查准率 查全率 F1 AUC Tree 0.9863 0.9887 0.9906 0.9896 0.9843 Random forest 0.9873 0.9896 0.9910 0.9903 0.9978 Adaboost 0.9885 0.9915 0.9910 0.9913 0.9965 Bagging 0.9916 0.9925 0.9948 0.9936 0.9979
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