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Sensors (Basel). 2019 Mar 4;19(5). pii: E1092. doi: 10.3390/s19051092.

Locating Hazardous Chemical Leakage Source Based on Cooperative Moving and Fixing Sensors.

Author information

1
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. ygkong@hdu.edu.cn.
2
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. moyingcolin@163.com.
3
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. zhs@hdu.edu.cn.
4
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. pjiang@hdu.edu.cn.
5
Quzhou Juhua Polyamide Fibre LLC, Quzhou 324000, China. jhwlpxl@163.com.
6
Quzhou Juhua Polyamide Fibre LLC, Quzhou 324000, China. yaoxuefeiqz@163.com.
7
Quzhou Juhua Polyamide Fibre LLC, Quzhou 324000, China. 13857098555@139.com.
8
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. HDU_musk@hotmail.com.
9
Automation, Hangzhou Dianzi University, Hangzhou 310000, China. 15958170231@163.com.

Abstract

In dealing with sudden hazardous chemical leakage accidents, the key to solving the evacuation and transfer of personnel and important property is to determine the location of the leakage source and the information of the source strength to gauge the scope of the impact of leakage. The particle swarm optimization algorithm with an adaptive mutation factor is applied to the inverse calculation of leakage source strength to obtain the leakage source information, and the leakage source location problem is transformed into an optimization problem. The mobile sensor is then introduced into the fixed sensor network. The mobile sensor moving strategy based on an extended Kalman filter is proposed. The estimated value of the previous moment and the current time are used to update the estimation of the state variable, and then the mobile strategy is planned. The interference of the random error of the optimization algorithm on the path planning of the mobile sensor is reduced by introducing the optimized result memory and, thus, location efficiency is improved. Simulation results showed that the proposed method, which combines mobile with fixed sensors, greatly expanded the monitoring function of the network, reduced the number of fixed sensors, and enhanced the positioning accuracy.

KEYWORDS:

adaptive mutation particle swarm optimization; cooperative localization; extended Kalman filtering; optimized result memory

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