Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1896-1903.doi: 10.13229/j.cnki.jdxbgxb20210194

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Vessel search method by earth observation satellite based on time⁃varying grid

Dan HU1,2(),Xin MENG1()   

  1. 1.Key Laboratory of Electronics and Information Technology for Space Systems，National Space Science Center，Chinese Academy of Sciences，Beijing 100190，China
    2.University of Chinese Academy of Sciences，Beijing 100049，China
  • Received:2021-03-15 Online:2022-08-01 Published:2022-08-12
  • Contact: Xin MENG E-mail:rwindwow@163.com;mengxin@nssc.ac.cn

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Abstract:

In order to improve the effectiveness， a time-varying grid model is proposed to divide the spatial and temporal dimensions of the region according to the satellite earth observation width and the maximum vessel speed. The vessel track prediction problem is transformed into a time-varying grid transition probability prediction problem， which effectively reduces the complexity of multi-step prediction. The multi-step time-varying grid transfer prediction with high accuracy is realized by the improved sequence to sequence（Seq2Seq） model， which has learned a large number of historical tracks in the research area. A satellite observation task planning algorithm for time-varying grids is designed and simulation experiments are carried out based on actual AIS data and satellite information. The results of experiments show that the Seq2Seq model based on deep learning has high accuracy in multi-step prediction of time-varying grids， which effectively improves the effectiveness of vessel search by earth observation satellites.

Key words: compute application, time-varying grid, vessel search, earth observation satellite, deep learning, sequence to sequence model

CLC Number: 

  • TP301.6

Fig.1

Diagram of vessel search based on observation satellite"

Fig.2

Diagram of time-varying grid divided"

Fig.3

Diagram of time-varying grid mapping"

Fig.4

Diagram of time-varying grid mapping"

Fig.5

Seq2Seq model for time-varying grids prediction"

Table 1

Information of satellites"

卫星名称卫星代号轨道高度/km倾角/（°）降交点地方时幅宽/km侧摆能力/（°）
资源一号04星ZY1-0477898.500010：30 AM60±32
资源三号01星ZY3-0150697.421010：30 AM51±32
资源三号02星ZY3-0250597.421010：30 AM51±32
高分一号卫星GF-0164598.050610：30 AM60±35
高分二号卫星GF-0263197.090810：30 AM45±35
实践九号A星SJ9-A64597.982010：30 AM30±35

Table 2

TLE of satellites"

卫星代号两行根数
ZY1-04

1 40336U 14079A 21041.78300343 -.00000110 00000-0 -23164-4 0 9997

2 40336 98.5363 117.3056 0001856 97.6028 262.5367 14.35423066323875

ZY3-01

1 38046U 12001A 21041.77982109 .00000761 00000-0 37025-4 0 9991

2 38046 97.3946 118.1644 0004704 78.3672 32.1382 15.21336706504859

ZY3-02

1 41556U 16033A 21041.88121295 .00000634 00000-0 31299-4 0 9999

2 41556 97.3402 117.4403 0000436 284.5074 203.7350 15.21367274261165

GF-01

1 39150U 13018A 21041.79204228 .00000042 00000-0 13123-4 0 9993

2 39150 97.8455 118.2646 0020503 70.0849 290.2565 14.76565123420188

GF-02

1 40118U 14049A 21041.74098995 .00000065 00000-0 15082-4 0 9992

2 40118 97.8174 119.3216 0009141 110.3054 249.9140 14.80653293350338

SJ9-A

1 38860U 12056A 21041.73090786 .00000043 00000-0 12358-4 0 9997

2 38860 97.6461 84.4101 0029174 174.9656 185.1845 14.79610380449670

Table 3

Parameters of seq2seq model"

参数名称参数值
编码器输入特征维度8
编码器隐层神经元数量128
编码器隐层层数4
编码器输出特征维度32
解码器输入特征维度32
解码器隐层神经元数量128
解码器隐层层数4
解码器输出特征维度18
训练批次大小32
训练轮数4000

Table 4

Minimum cross entropy loss"

输入数据长度输出数据长度最小交叉熵
30152.81×10-6
30204.78×10-6
30259.72×10-6
20206.12×10-6
40204.11×10-6

Fig.6

Cross entropy loss curves of seq2seq model"

Table 5

Windows of satellites access to active region"

序号窗口开始时间窗口结束时间过区域次数
12019-01-02 22∶53∶492019-01-03 02∶13∶4411
22019-01-03 23∶11∶172019-01-04 01∶00∶5310
32019-01-05 23∶41∶392019-01-06 01∶49∶1310
42019-01-11 22∶48∶542019-01-12 02∶01∶1211
52019-01-14 23∶29∶342019-01-15 02∶07∶028
62019-01-23 23∶17∶272019-01-24 01∶12∶388

Table 6

Experiment results of vessel search used by satellites"

序号基于高斯分布船舶运动预测的搜索方法基于时变网格的船舶搜索方法
最早搜索到船舶时间延时/min搜索到船舶次数最早搜索到船舶时间延时/min搜索到船舶次数
1117.503117.374
2/0169.972
3/0178.752
4110.482110.472
5/082.252
6/087.851
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