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浙江大学学报(工学版)
浙江大学学报(工学版)  2019, Vol. 53 Issue (4): 753-760    DOI: 10.3785/j.issn.1008-973X.2019.04.016
土木工程、海洋工程     
基于成像声呐的水下多目标跟踪研究
荆丹翔(),韩军*(),徐志伟,陈鹰
浙江大学 海洋学院，浙江 舟山 316021
Underwater multi-target tracking using imaging sonar
Dan-xiang JING(),Jun HAN*(),Zhi-wei XU,Ying CHEN
Ocean College, Zhejiang University, Zhoushan 316021, China
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摘要：

针对水下多目标跟踪问题，提出基于成像声呐的高效目标跟踪算法. 基于声呐的成像特点，针对声学图像中的每个像素点，建立基于信号强度的回波信号模型，提取图像中的个体目标. 采用基于序贯蒙特卡罗的概率密度假设(SMCPHD)滤波对各目标状态进行滤波，结合Auction航迹识别算法将滤波后的目标状态与已确定的航迹进行关联，实现多目标跟踪. 通过算法的仿真分析发现，该方法相对于基于数据关联型的多目标跟踪算法如联合概率数据关联(JPDA)算法、多假设跟踪(MHT)算法，大大提高了计算效率. 对采集的现场数据进行目标提取与跟踪，获得目标的跟踪轨迹.
关键词： 成像声呐;  目标提取;  多目标跟踪;  航迹识别;  目标轨迹    
Abstract:

An efficient target tracking algorithm based on an imaging sonar was proposed to solve the problem of underwater multi-target tracking. The echo signal model based on signal intensity was established for each pixel point in the acoustic image according to the imaging features of the sonar in order to extract the individual target from the images. The sequential Monte Carlo probability hypothesis density (SMCPHD) filtering was applied to the target states. The Auction track recognition algorithm was used to associate the filtered target states with the identified tracks, so that the multi-target tracking was realized. The simulation analysis of the algorithm showed that the proposed method was more efficient than the multi-target tracking algorithms based on data correlation, eg. joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT). A field experiment was conducted to collect the sonar data. The tracking trajectories of all the targets were obtained after the target extraction and tracking.
Key words: imaging sonar    target extraction    multi-target tracking    track recognition    target trajectory
收稿日期: 2018-03-07 出版日期: 2019-03-28
CLC:  TP 29  
通讯作者: 韩军     E-mail: jingdxiang@zju.edu.cn;jhan@zju.edu.cn
作者简介: 荆丹翔（1990—），男，博士生，从事水声探测的研究. orcid.org/0000-0002-2080-2604. E-mail： jingdxiang@zju.edu.cn
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引用本文:

荆丹翔,韩军,徐志伟,陈鹰. 基于成像声呐的水下多目标跟踪研究[J]. 浙江大学学报(工学版), 2019, 53(4): 753-760.

Dan-xiang JING,Jun HAN,Zhi-wei XU,Ying CHEN. Underwater multi-target tracking using imaging sonar. Journal of ZheJiang University (Engineering Science), 2019, 53(4): 753-760.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.04.016    或    http://www.zjujournals.com/eng/CN/Y2019/V53/I4/753

图 1  声呐工作示意图
图 2  航迹识别算法原理图
图 3  航迹识别算法流程图
目标 起始位置/m 初始速度/（m?s?1） 出现时间/s 消失时间/s
目标1 （0, 0） （3, ?3） 1 20
目标2 （20, 30） （3, ?3） 15 40
目标3 （30, ?40） （3, 3） 35 50
表 1  各目标运动参数
图 4  目标沿着x、y轴方向的跟踪
图 5  SMCPHD滤波结果
图 6  目标个数的估计值与真实值
图 7  Wasserstein距离
图 8  3个目标航迹识别的结果图
图 9  DIDSON布置示意图
参数 参数值 参数 参数值
发射频率 1.8 MHz 接收增益 20 dB
波束个数 96 声速 1 457 m/s
采样点 512 倾斜角 10°
采样频率 37.3 kHz 帧率 13帧/s
表 2  DIDSON的配置参数
图 10  声呐原图和目标提取的结果
坐标参数 t0 t1 t2 t3 t4 t5
ρ1/m 7.71 7.66 7.59 7.48 7.37 7.25
θ1/rad 1.49 1.53 1.57 1.61 1.66 1.70
ρ2/m 7.51 7.44 7.35 7.13 7.05 6.90
θ2/rad 1.51 1.57 1.59 1.61 1.64 1.66
ρ3/m 7.42 7.31 7.23 7.13 7.03 6.83
θ3/rad 1.46 1.49 1.53 1.57 1.60 1.64
ρ4/m 7.36 7.29 7.17 7.03 6.97 6.82
θ4/rad 1.45 1.47 1.51 1.56 1.59 1.63
ρ5/m 6.9 6.82 6.72 6.66 6.55 6.47
θ5/rad 1.48 1.54 1.57 1.62 1.69 1.71
ρ6/m 6.94 6.58 6.51 6.41 6.42 6.38
θ6/rad 1.44 1.49 1.52 1.57 1.64 1.72
ρ7/m 6.33 6.33 6.35 6.41 6.50 6.57
θ7/rad 1.45 1.49 1.53 1.56 1.59 1.62
ρ8/m 6.17 6.17 6.2 6.25 6.34 6.41
θ8/rad 1.44 1.49 1.51 1.55 1.58 1.60
ρ9/m 4.69 4.78 4.84 5.00 5.07 5.17
θ9/rad 1.52 1.54 1.57 1.60 1.62 1.63
ρ10/m 4.39 4.54 4.75 ? ? ?
θ10/rad 1.50 1.52 1.60 ? ? ?
ρ11/m 3.17 3.26 3.38 3.44 3.48 3.61
θ11/rad 1.49 1.51 1.56 1.63 1.66 1.67
表 3  各目标在声呐图中的位置
图 11  目标对应的坐标系
图 12  跟踪轨迹在二维平面及三维空间的显示
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