Volume 8 Issue 1
Mar.  2019
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Article Contents
XU Yasheng, DING Chibiao, REN Wenjuan, et al. Multi-feature combination track-to-track association based on histogram statistics feature[J]. Journal of Radars, 2019, 8(1): 25–35. doi: 10.12000/JR18028
Citation: XU Yasheng, DING Chibiao, REN Wenjuan, et al. Multi-feature combination track-to-track association based on histogram statistics feature[J]. Journal of Radars, 2019, 8(1): 25–35. doi: 10.12000/JR18028

Multi-feature Combination Track-to-track Association Based on Histogram Statistics Feature

DOI: 10.12000/JR18028
Funds:  The National Natural Science Foundation of China (61725105, 61331017)
More Information
  • Corresponding author: XU Yasheng, xuyasheng93@163.com
  • Received Date: 2018-03-29
  • Rev Recd Date: 2018-05-29
  • Available Online: 2018-07-09
  • Publish Date: 2019-02-28
  • 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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  • [1]
    杨威, 陈杰, 李春升. 面向目标特性精细提取的SAR数据融合成像处理方法[J]. 雷达学报, 2015, 4(1): 29–37. doi: 10.12000/JR15017

    YANG Wei, CHEN Jie, and LI Chun-sheng. SAR data fusion imaging method oriented to target feature extraction[J]. Journal of Radars, 2015, 4(1): 29–37. doi: 10.12000/JR15017
    [2]
    李伟, 王兴亮, 邹鲲, 等. 基于数据融合和陷波滤波的MIMO雷达抗欺骗干扰算法[J]. 雷达学报, 2012, 1(3): 246–252. doi: 10.3724/SP.J.1300.2012.20060

    LI Wei, WANG Xing-liang, ZOU Kun, et al. Anti deceptive jamming for MIMO radar based on data fusion and notch filtering[J]. Journal of Radars, 2012, 1(3): 246–252. doi: 10.3724/SP.J.1300.2012.20060
    [3]
    LIGGINS M E, HALL D L, and LLINAS J. Handbook of Multisensor Data Fusion: Theory and Practice[M]. 2nd ed., Boca Raton, FL: CRC Press, 2009.
    [4]
    何友, 王国宏, 陆大䋮, 等. 多传感器信息融合及应用[M]. 第2版, 北京: 电子工业出版社, 2007.

    HE You, WANG Guo-hong, LU Da-jin, et al. Multisensor Information Fusion with Applications[M]. 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.
    [5]
    CHANG C and YOUENS L C. Measurement correlation for multiple sensor tracking in a dense target environment[J]. IEEE Transactions on Automatic Control, 1982, 27(6): 1250–1252. doi: 10.1109/TAC.1982.1103107
    [6]
    Bar-Shalom Y. On the track-to-track correlation problem[J]. IEEE Transactions on Automatic Control, 1981, 26(2): 571–572. doi: 10.1109/TAC.1981.1102635
    [7]
    AZIZ A M, TUMMALA M, and CRISTI R. Fuzzy Logic Data Correlation Approach in Multisensor-Multitarget Tracking Systems[M]. Elsevier North-Holland, Inc, 1999. https://www.sciencedirect.com/science/article/pii/S0165168499000080.
    [8]
    SMITH III J F. Fuzzy logic multisensor association algorithm[C]. Proceedings of SPIE Signal Processing, Sensor Fusion, and Target Recognition VI, Orlando, FL, USA, 1997: 76–87. doi: 10.1117/12.280788.
    [9]
    YANG Y T, LIANG Y, YANG Y B, et al. Asynchronous track-to-track association algorithm based on dynamic time warping distance[C]. Proceedings of the 34th Control Conference, Hangzhou, China, 2015: 4772–4777.
    [10]
    董凯, 王海鹏, 刘瑜. 基于拓扑统计距离的航迹抗差关联算法[J]. 电子与信息学报, 2015, 37(1): 50–55. doi: 10.11999/JEIT140244

    DONG Kai, WANG Hai-peng, and LIU Yu. Anti-bias track association algorithm based on topology statistical distance[J]. Journal of Electronics &Information Technology, 2015, 37(1): 50–55. doi: 10.11999/JEIT140244
    [11]
    STONE L D, TRAN T M, and WILLIAMS M L. Improvement in track-to-track association from using an adaptive threshold[C]. Proceedings of the 12th International Conference on Information Fusion, Seattle, WA, USA, 2009: 1627–1633.
    [12]
    AGRAWAL R, FALOUTSOS C, and SWAMI A N. Efficient similarity search in sequence databases[C]. Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, Berlin, Heidelberg, Germany, 1993: 69–84.
    [13]
    BARCZEWSKA K and DROZD A. Comparison of methods for hand gesture recognition based on dynamic time warping algorithm[C]. Proceedings of 2013 Federated Conference on Computer Science and Information Systems, Poland, 2013: 207–210.
    [14]
    BANKÓ Z and ABONYI J. Correlation based dynamic time warping of multivariate time series[J]. Expert Systems with Applications, 2012, 39(17): 12814–12823. doi: 10.1016/j.eswa.2012.05.012
    [15]
    CASACUBERTA F, VIDAL E, and RULOT H. On the metric properties of dynamic time warping[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1987, 35(11): 1631–1633. doi: 10.1109/TASSP.1987.1165065
    [16]
    HUANG B and KINSNER W. ECG frame classification using dynamic time warping[C]. Proceedings of 2002 Canadian Conference on Electrical and Computer Engineering, Winnipeg, Manitoba, Canada, 2002: 1105–1110.
    [17]
    JABLONSKI B. Quaternion dynamic time warping[J]. IEEE Transactions on Signal Processing, 2012, 60(3): 1174–1183. doi: 10.1109/TSP.2011.2177832
    [18]
    FU C X, ZHANG P L, JIANG J, et al. A Bayesian approach for sleep and wake classification based on dynamic time warping method[J]. Multimedia Tools and Applications, 2017, 76(17): 17765–17784. doi: 10.1007/s11042-015-3053-z
    [19]
    BAUMANN M, OZDOGAN M, RICHARDSON A D, et al. Phenology from landsat when data is scarce: Using MODIS and dynamic time-warping to combine multi-year landsat imagery to derive annual phenology curves[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 54: 72–83. doi: 10.1016/j.jag.2016.09.005
    [20]
    VARATHARAJAN R, MANOGARAN G, PRIYAN M K, et al. Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm[J]. Cluster Computing, 2017(6): 1–10. doi: 10.1007/s10586-017-0977-2
    [21]
    ZHOU N C, WANG J, and WANG Q G. A novel estimation method of metering errors of electric energy based on membership cloud and dynamic time warping[J]. IEEE Transactions on Smart Grid, 2017, 8(3): 1318–1329. doi: 10.1109/TSG.2016.2619375
    [22]
    WAN Y, CHEN X L, and SHI Y. Adaptive cost dynamic time warping distance in time series analysis for classification[J]. Journal of Computational and Applied Mathematics, 2017, 319: 514–520. doi: 10.1016/j.cam.2017.01.004
    [23]
    SINGER R A and KANYUCK A J. Computer Control of Multiple Site Track Correlation[M]. Tarrytown, NY, USA: Pergamon Press, Inc., 1971.
    [24]
    DITZLER W R. A demonstration of multisensor tracking[C]. Proceedings of the 1987 Tri-Service Data Fusion Symposium, JHU/APL, Laurel, Md., 1987: 303–311.
    [25]
    周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.

    ZHOU Zhi-hua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016.
    [26]
    周晓安. VTS系统中多传感器目标航迹关联算法研究[D]. [硕士论文], 南京信息工程大学, 2014.

    ZHOU Xiao-an. The research of track association for multi-sensor target in VTS system[D]. [Master dissertation], Nanjing University of Information Science & Technology, 2014.
    [27]
    周晓安, 高贵明. 自适应阈值航迹关联算法与实现[J]. 现代防御技术, 2014, 42(4): 193–198. doi: 10.3969/j.issn.1009-086x.2014.04.031

    ZHOU Xiao-an and GAO Gui-ming. Adaptive threshold algorithm and implementation for track-to-track association[J]. Modern Defense Technology, 2014, 42(4): 193–198. doi: 10.3969/j.issn.1009-086x.2014.04.031
    [28]
    HAN Jia-wei and KAMBER M. 数据挖掘: 概念与技术[M]. 范明, 孟小峰, 译. 北京: 机械工业出版社, 2007.

    HAN Jia-wei and KAMBER M. Data Mining: Concepts and Techniques[M]. FAN Ming, MENG Xiao-feng, trans. Beijing: China Machine Press, 2007.
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