Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 1187-1199.doi: 10.13229/j.cnki.jdxbgxb.20210811

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Seismic sensing perimeter security system based on cat swarm algorithm

Qiu-zhan ZHOU1(),Ze-yu JI1,Cong WANG1(),Ji-kang HU1,Ming-ming LI2,Yu-zhu CHEN1,Xian-feng ZHOU3,4,Ping-ping LIU5   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.Big Data & Management Center，Jilin University，Changchun 130012，China
    3.Key Laboratory of Geoexploration Instrumentation of Ministry of Education，Jilin University，Changchun 130061，China
    4.College of Instrumentation & Electrical Engineering，Jilin University，Changchun 130061，China
    5.College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2021-08-22 Online:2023-04-01 Published:2023-04-20
  • Contact: Cong WANG E-mail:13504465154@163.com;wangcong2020@jlu.edu.cn

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Abstract:

Traditional perimeter-security systems either suffer from low positioning accuracy of the target or cannot determine the optimal result generated by multiple groups of sensors in common TDOA. In response to the above issues， this paper designs an intelligent perimeter-security system， by means of seismic sensors and cat-swarm algorithm. First， moving coil seismic sensors are used for collecting the seismic signals generated by targets on shallow surfaces. Then， the collected signals are processed to obtain initial TDOA localization results of the target. Later， these initial results are further optimized via cat-swarm algorithm. Aiming at different application scenarios of the system， this paper designs two communication modes （i.e.， LoRa and Beidou short message） to ensure system communication in remote regions. In order to verify the effectiveness of the optimization algorithm， several experiments are conducted in real world under different parameter settings. According to the experimental results， when the error in propagation velocity of seismic wave is less 10%， the average localization error of the proposed system （i.e.， optimized by cat-swarm algorithm） is less 1 meter. In particular， compared with the initial TDOA localization results， the localization error is reduced by 46.9%.

Key words: communication and information system, perimeter-security, TDOA location, swarm intelligence, cat-swarm algorithm

CLC Number: 

  • TP274

Fig.1

Architecture of the proposed swarm intelligence perimeter-security system"

Fig.2

Swarm intelligence perimeter-security system"

Fig.3

Cat-swarm algorithm flow"

Fig.4

Real-world system composition"

Fig.5

Software for system configuration"

Fig.6

Sensor layout of system"

Fig.7

TDOA localization results before and afteroptimized by cat swarm-algorithm"

Table 1

Optimization results of simulated data indifferent locations"

目标位置TDOA横坐标TDOA纵坐标最优解与震源距离/m
优化前优化后
（7.5，2.5）6~71~20.83260.0272
（7.5，2.5）8~93~40.83060.0445
（7.5，2.5）6~73~40.87820.0309

Fig.8

Diamond layout of sensors"

Fig.9

Circular layout of sensors"

Table 2

Algorithm optimization results of simulated data in diamond layout of sensors"

目标位置TDOA横坐标范围TDOA纵坐标范围优化前最优解与震源距离/m优化后最优解与震源距离/m
（10，10）9~109~100.21680.0120
（10，10）9~1010~110.15630.1044
（10，10）10~119~100.39240.1562
（10，10）10~1110~110.16430.1518
（10，10）9~119~110.15590.0150

Table 3

Algorithm optimization results of simulated data in circular layout of sensors"

目标位置TDOA横坐标范围TDOA纵坐标范围优化前最优解与震源距离/m优化后最优解与震源距离/m
（10，10）9~109~100.13270.0073
（10，10）9~1010~110.30370.1315
（10，10）10~119~100.27490.0925
（10，10）10~1110~110.18460.1277
（10，10）9~119~110.08540.0036

Table 4

Algorithm optimization results of simulated data in random layout of sensors"

目标位置TDOA横坐标范围TDOA纵坐标范围优化前最优解与震源距离/m优化后最优解与震源距离/m
（10，10）9~109~100.13410.0182
（10，10）9~1010~110.30980.0972
（10，10）10~119~100.32540.1211
（10，10）10~1110~110.16910.1588
（10，10）9~119~110.40140.0435

Fig.10

Error analysis of cat-swarm algorithm under different speed errors"

Table 5

Error analysis of 50 positioning results underdifferent vibration wave velocity errors"

速度误差范围50次定位结果中误差最大值/m50次定位结果中误差最小值/m50次定位结果中误差均值/m
00.20510.000210.0437
5%1.05650.02130.3879
10%2.78610.00710.7610
20%5.66610.05931.9049

Fig.11

TDOA localization results before and afteroptimized by cat swarm-algorithm"

Table 6

Execution time of optimization algorithm under different iterations"

迭代次数算法执行时间/s
500.4992
1000.9318
5004.3264
10008.3203

Fig.12

Comparison of localization results before andafter optimized by cat swarm-algorithm"

Fig.13

Comparison of localization results afteroptimized by three algorithms"

Fig.14

Comparison of evolution curves of threealgorithms"

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