摘要
针对地下浅层复杂空间中震源定位精度低,定位速度慢的问题,提出基于双种群量子粒子群(DIR-QPSO)联合可控波束形成(SRP)的震源定位算法。该方法首先基于Logistic混沌模型的全局遍历性构建震源定位的初始化种群;其次,利用DIR-QPSO主辅粒子群之间的相互约束,消除震源搜索过程中出现的局部收敛的现象;同时,利用SRP定位模型在空间中能量聚焦的特性,构建DIR-QPSO的适应度函数,实现大区域的精准搜索。在实际实验环境下,将该定位算法与QPSO联合走时偏振角度的算法进行对比,实验结果表明,该算法相比QPSO联合走时偏振角度的算法,提升了粒子搜索范围,使得收敛区域更加准确,同时定位精度在0.235 m以内,均方根误差(MSE)也减小至0.0003左右,算法总收敛时间最大减少了0.5392 s。由此可见该算法在定位速度和精度、粒子搜索范围、收敛区域等方面具有较强的优势,基本满足地下浅层复杂空间中震源定位的需求,有较强的实际应用价值。
Aiming at the problem of low accuracy and slow localization speed of hypocenters in complex shallow underground space,a source location algorithm based on the dual-group interaction quantum-behaved particle swarm optimization algorithm based on random evaluation(DIR-QPSO)combined with Steered-Response Power(SRP)was proposed.The method firstly constructed the initialized population of source location based on the global ergodicity of the Logistic chaotic model;secondly,the mutual constraint between the main and auxiliary particle swarms of DIR-QPSO was used to eliminate the phenomenon of local convergence in the search process of the source;The SRP positioning model has the characteristics of energy focusing in space,and the fitness function of DIR-QPSO was constructed to realize accurate search in large areas.Finally,in the actual experimental environment,the positioning algorithm and the QPSO combined travel time polarization angle algorithm were compared.The experimental results showed that,compared with the QPSO combined traveltime polarization angle algorithm,this algorithm improved the particle search range and makes the convergence area more accurate.At the same time,the positioning accuracy was within 0.235 m,and the root mean square error(MSE)was also reduced to about 0.0003.The total convergence time of the algorithm was reduced by 0.5392 s at most.It could be seen that this algorithm had strong advantages in positioning speed and accuracy,particle search range,convergence area,etc.
作者
庞珂
李剑
苏新彦
刘晓佳
魏晓曼
孙袖山
PANG Ke;LI Jian;SU Xinyan;LIU Xiaojia;WEI Xiaoman;SUN Xiushan(State Key Laboratory of Dynamic Testing Technology,North University of China,Taiyuan 030051,China;Key Laboratory of Information Detection and Processing of Shanxi Province,North University of China,Taiyuan 030051,China)
出处
《探测与控制学报》
CSCD
北大核心
2023年第2期92-98,共7页
Journal of Detection & Control
