Automation, Hangzhou Dianzi University, Hangzhou 310000, China.
Quzhou Juhua Polyamide Fibre LLC, Quzhou 324000, China.
Sensors (Basel). 2019 Mar 4;19(5):1092. doi: 10.3390/s19051092.
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.
在处理突发危险化学品泄漏事故时，解决人员和重要财产疏散转移的关键是确定泄漏源位置和源强信息，以衡量泄漏影响的范围。将具有自适应变异因子的粒子群优化算法应用于泄漏源强度的反演计算，以获取泄漏源信息，并将泄漏源位置问题转化为优化问题。然后将移动传感器引入固定传感器网络中。提出了基于扩展卡尔曼滤波器的移动传感器移动策略，利用上一时刻的估计值和当前时刻的观测值来更新状态变量的估计值，然后规划移动策略。通过引入优化结果记忆，减少了优化算法的随机误差对移动传感器路径规划的干扰，从而提高了定位效率。仿真结果表明，将移动传感器与固定传感器相结合的方法极大地扩展了网络的监测功能，减少了固定传感器的数量，提高了定位精度。
