吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (4): 1187-1199.doi: 10.13229/j.cnki.jdxbgxb.20210811

• 通信与控制工程 • 上一篇    

基于猫群算法的震动感知周界安防系统

周求湛1(),冀泽宇1,王聪1(),胡继康1,李明明2,陈禹竺1,周险峰3,4,刘萍萍5   

  1. 1.吉林大学 通信工程学院，长春 130012
    2.吉林大学 大数据与管理中心，长春 130012
    3.吉林大学 地球信息探测仪器教育部重点实验室，长春 130061
    4.吉林大学 仪器科学与电气工程学院，长春 130061
    5.吉林大学 计算机科学与技术学院，长春 130012
  • 收稿日期:2021-08-22 出版日期:2023-04-01 发布日期:2023-04-20
  • 通讯作者: 王聪 E-mail:13504465154@163.com;wangcong2020@jlu.edu.cn
  • 作者简介:周求湛（1974-），男，教授，博士.研究方向：微弱信号检测.E-mail：13504465154@163.com
  • 基金资助:
    国家自然科学基金项目(62071199);吉林省自然科学基金项目(20200201283JC)

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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摘要：

针对传统周界安防系统入侵目标定位方法定位精度较差且常用的基于信号到达时间差（TDOA）定位方法在面对多组传感器情况难以确定最优解的问题，本文设计了基于猫群算法的震动感知智能周界安防系统。首先利用动圈式振动传感器采集入侵目标在浅地表产生的振动信号，并通过振动信号对入侵目标进行TDOA定位。然后通过猫群算法对TDOA初步定位结果进行优化。最后针对系统的不同应用场景，本文设计实现LoRa无线通信和北斗短报文无线通信两种通信模式，解决了系统在偏远地区部署的通信问题。为验证本文算法的有效性，设计了不同算法参数环境下的验证实验以及在实际环境下的测试实验。结果表明：本文算法优化的目标定位方法在浅地表震动波传播速度误差小于自身10%的情况下，平均定位误差小于1 m。算法定位误差相较于TDOA初步定位结果减小约46.9%。

关键词: 通信与信息系统, 周界安防, TDOA定位, 群体智能, 猫群算法

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

中图分类号: 

  • TP274

图1

群体智能周界安防系统架构"

图2

群体智能周界安防系统实物图"

图3

猫群算法流程图"

图4

实际系统组成图"

图5

系统上位机配置软件"

图6

系统传感器布设示意图"

图7

猫群算法优化前后的TDOA定位结果"

表1

不同位置模拟数据算法优化结果"

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

图8

传感器菱形布设示意图"

图9

传感器圆形布设示意图"

表2

传感器菱形布设方式模拟数据算法优化结果"

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

表3

传感器圆形布设方式模拟数据算法优化结果"

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

表4

随机布设方式模拟数据算法优化结果"

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

图10

不同速度误差下猫群算法误差"

表5

不同震动波速度误差下50次定位结果误差"

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

图11

猫群算法优化前后的TDOA定位结果"

表6

不同迭代次数下优化算法的执行时间"

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

图12

猫群算法优化前后定位结果对比"

图13

3种算法优化后定位结果对比"

图14

3种算法进化曲线对比"

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