基于物理模型法的目标地震动信号建模与仿真
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摘要
在战场地面目标探测的研究中,由于地形地势复杂多变,为了获得典型目标(人员、车辆)在各种地形地质条件及各种运动状态下充分的样本信号,提出了一种基于弹簧阻尼等效物理模型的建模方法。该方法通过对目标与地面作用过程及地震动信号传播机理的分析,运用物理模型推导得出目标激励函数;并主要以弹性波动方程中赫姆霍兹解法为基础,最终建立了信号传播介质微粒的速度模型。而后在MATLAB环境下,利用该模型仿真得到不同条件下的目标地震动预测信号。仿真结果表明,预测信号与实测信号符合很好,目标信号模型构建正确。该模型将对目标地震动信号库的建立具有重要意义。
In the study of ground targets detection,in order to obtain each type of typical target( person、vehicles) sample signals in the various topography conditions and motion states,a modeling method based on spring damping equivalent physical model was proposed due to the complexity of the battlefield. In the method,the interaction process of the target and ground and signal propagation mechanism was analyzed,the target stimulated function was derived by the model. The targets signal medium particle velocity model was established based on Helmholtz equation solution. And then in the MATLAB environment,target seismic predicted signals under the different conditions were simulated by using the model. Results show that the predicted signal was similar to the measured signal proved that the model was correct,the model will play an important role in the establishment of the target seismic signal library.
In the study of ground targets detection,in order to obtain each type of typical target( person、vehicles) sample signals in the various topography conditions and motion states,a modeling method based on spring damping equivalent physical model was proposed due to the complexity of the battlefield. In the method,the interaction process of the target and ground and signal propagation mechanism was analyzed,the target stimulated function was derived by the model. The targets signal medium particle velocity model was established based on Helmholtz equation solution. And then in the MATLAB environment,target seismic predicted signals under the different conditions were simulated by using the model. Results show that the predicted signal was similar to the measured signal proved that the model was correct,the model will play an important role in the establishment of the target seismic signal library.
引文
1 孙秀超.多通道地震动信号采集与处理系统的研究与实现.南京:南京理工大学,2006Sun X C.The study on multi-channel seismic signal acquisition and processing system.Nanjing:Nanjing University of Science and Technology,2006
2 宋先海,李刚.瑞雷波多模式叠加耦合机理研究.工程勘察,2011;39(5):81—85Song X H,Li G,Study on coupling mechanism of multi-mode superposition of rayleigh wave.Geotechnical Investigation&Surveying,2011;39(5):81—85
3 聂伟荣.多传感器探测与控制网络技术——地面运动目标震动信号探测与识别.南京:南京理工大学,2001Nie W R.Detecting and controlling network technology with multisensor system.Nanjing:Nanjing University of Science and Technology,2001
4 赵玉民.战场监视地面传感器系统技术与应用.北京:北京理工大学出版社,2011:32—34Zhao Y M.Technology and application about battlefield ground monitoring seismic sensors system.Beijing:Beijing University of Science and Technology Press,2011:32—34
5 乔艳红.瑞雷波在混凝土无损检测中的应用研究.北京:中国地质大学,2008Qiao Y H.Research on Rayleigh wave method and its application to concrete nondestructive testing.Beijing:China University of Geosciences,2008
6 崔丹丹,张才千,韩东.基于BP神经网络的路面不平度检测与仿真.计算机仿真,2014:31(5):162—166Cui D D,Zhang C Q,Han D.Road roughness detection simulation based on BP neural network.Journal of Computer Simulation,2014;31 (5):162—166
2 宋先海,李刚.瑞雷波多模式叠加耦合机理研究.工程勘察,2011;39(5):81—85Song X H,Li G,Study on coupling mechanism of multi-mode superposition of rayleigh wave.Geotechnical Investigation&Surveying,2011;39(5):81—85
3 聂伟荣.多传感器探测与控制网络技术——地面运动目标震动信号探测与识别.南京:南京理工大学,2001Nie W R.Detecting and controlling network technology with multisensor system.Nanjing:Nanjing University of Science and Technology,2001
4 赵玉民.战场监视地面传感器系统技术与应用.北京:北京理工大学出版社,2011:32—34Zhao Y M.Technology and application about battlefield ground monitoring seismic sensors system.Beijing:Beijing University of Science and Technology Press,2011:32—34
5 乔艳红.瑞雷波在混凝土无损检测中的应用研究.北京:中国地质大学,2008Qiao Y H.Research on Rayleigh wave method and its application to concrete nondestructive testing.Beijing:China University of Geosciences,2008
6 崔丹丹,张才千,韩东.基于BP神经网络的路面不平度检测与仿真.计算机仿真,2014:31(5):162—166Cui D D,Zhang C Q,Han D.Road roughness detection simulation based on BP neural network.Journal of Computer Simulation,2014;31 (5):162—166
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