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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (4): 753-760    DOI: 10.3785/j.issn.1008-973X.2019.04.016
    
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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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     
Received: 07 March 2018      Published: 28 March 2019
CLC:  TP 29  
Corresponding Authors: Jun HAN     E-mail: jingdxiang@zju.edu.cn;jhan@zju.edu.cn
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Dan-xiang JING
Jun HAN
Zhi-wei XU
Ying CHEN
Cite this article:

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.

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


基于成像声呐的水下多目标跟踪研究

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 
Fig.1 Working diagram of DIDSON
Fig.2 Schematic diagram of track recognition algorithm
Fig.3 Flow chart of track recognition algorithm
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
Tab.1 Motion parameters of targets
Fig.4 Targets tracking along x-axis and y-axis
Fig.5 Results of SMCPHD filtering
Fig.6 Estimation and true value of target number
Fig.7 Wasserstein distance
Fig.8 Chart of three targets after track recognition
Fig.9 Placement schematic illustration of DIDSON
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
Tab.2 Configuration parameters of DIDSON
Fig.10 Raw image and result of target extraction
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
Tab.3 All targets’ positions obtained from sonar image
Fig.11 Coordinate system for target
Fig.12 Tracking trajectories shown in two-dimensional plane and three-dimensional space
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