智能光纤传感器网络地面目标识别若干问题研究
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摘要
无人值守地面传感器网络(UGS)最早是在战争中发展起来的,对地面目标进行探测、分类、跟踪和定位的智能侦察系统。光纤传感器网络有着灵敏度高,不受电磁干扰,与光纤通信网络兼容,易于大规模组网等诸多优势,所以成为无人值守地面传感器系统的有力补充。
地震动传感器是地面传感器网络的重要组成部分,光纤地震动传感器网络成为战场环境监测的重要手段。在战场环境下,当地面目标运动时,所激发的地面振动信号沿地球表面向四面传播,到达传感器端,通过对信号的分析、处理,就可以实现对地面目标的识别、定位和跟踪等。本文讨论了战场环境下,光纤地震动传感器网络对地面目标的探测和识别相关问题。
本文首先研究了地面运动目标的地震动信号的发生和传播机理。对地面人员和车辆信号的目标特性进行了分析,分析了地面人员目标的探测方法,建立了地面车辆目标的信号发生的模型。然后,着重讨论地震波的传播、衰减、频散等各方面的影响,通过波动方程定性的分析了传播过程对信号本身特性及其识别带来的影响。然后,分析实际外场环境下可能的背景噪声状况,提出了基于自适应阈值的恒虚警率目标探测方法,在保证探测率的同时,极大程度的降低了系统虚警率。
在此基础上,对地面目标信号进行了特征的提取和优化。分别从信号的时频、频域和时频域分析了信号的基本特性。重点讨论了地面人员特征量的提取,运用自适应阈值小波去噪方法对地面人员信号进行了去噪处理,极大程度的提高了信号的信噪比。地面车辆信号在时频域具有明显的特征,与频域相结合,提出了多特征联合的特征提取方法。根据外场实际采集的目标样本,建立了大规模的目标特征库。
为了应对外界复杂情况,提高识别率,采用了基于统计学习理论和支持向量机的目标识别方法,通过与其它模式识别方法的对比实验,说明支持向量机的分类方法具有更好分类能力和泛化性,在目标识别领域有广阔的应用前景。
在光纤传感器网络中,多传感器协同处理是提高系统整体性能的基本方法,本文采用基于DS证据理论的判决融合方法,提高了系统的分类正确率。在实际外场应用中,多混叠目标的探测与识别问题是亟待研究和解决的问题。本文首次采用独立分量分析的方法对多混叠目标的地震动信号进行了分析,首先将混叠信号进行分离,然后再分别进行识别。建立了多混叠信号的基本模型,运用FastICA算法进行了仿真实验,说明独立成分分析是解决多目标问题的有效算法。
In this paper, the problems about ground target detection, classificationin fiber seismic senor network are discussed. The unattended ground sensor(UGS) system was developed in military for ground target detection, classificationand tracking in battle field surveillance. The fiber seismic senors have a lotof advantages such as high sensitivity, electromagnetic resistance and networkorganizing easiness. The fiber sensor network has been an important complementfor ground sensor network. In the battle field, the movement of ground targetsuch as personnel, tracked vehicle or wheeled vehicle motivates the seismic wavesthat propagating along with the ground surface. The seismic signal received bythe seismic senor can be analyzed for target classification.
First, the generation and propagation of seismic signal caused by movingtarget is discussed. An detection method of personnel footstep is discussed ac-cording to its impulse nature. Then a model of vehicle vibration when movingis built that can be used to analyze the signal characteristic. The propaga-tion model of seismic wave is more complicated to be analyzed. In propagationprocedure, the amplitude of the signal is attenuated, and the frequency has dis-persion. An improved constant false alarm rate (CFAR) detection method basedon adaptive threshold is used to decrease the false alarm in high backgroundnoise. Second, the feature extraction and selection of seismic signal is discussed.The traditional feature extraction methods in time domain, frequency domainand time-frequency domain are discussed, and an united multi-feature method isproposed. The principle component analysis (PCA) is used to reduce the dimen-sion of the feature vector. According to real-life ground target signal acquired inthe field experiment, a large feature data set of di?erent targets is built. Third,the ground target classification method based on statistical learning theory andsupport vector machine is discussed. The traditional classifiers such as Bayes clas-sifier, neural network have some disadvantages when the sample number is small.The comparison experiment shows that the support vector machine (SVM) clas- sifier outperforms the other method, and have bright future in this application.At last, the problems about multi-sensor and multi-target detection and recog-nition are discussed. The decision fusion strategy based on DS evidence theoryis used to improve the overall performance of sensor network. The multi-targetmixed signal processing is a di?cult problem to be tacked in sensor network. Weuse the independent component analysis (ICA) to separate the mixed signal, andthen to classify them separately. The emulation experiment shows that the ICAis an e?ective method.
地震动传感器是地面传感器网络的重要组成部分,光纤地震动传感器网络成为战场环境监测的重要手段。在战场环境下,当地面目标运动时,所激发的地面振动信号沿地球表面向四面传播,到达传感器端,通过对信号的分析、处理,就可以实现对地面目标的识别、定位和跟踪等。本文讨论了战场环境下,光纤地震动传感器网络对地面目标的探测和识别相关问题。
本文首先研究了地面运动目标的地震动信号的发生和传播机理。对地面人员和车辆信号的目标特性进行了分析,分析了地面人员目标的探测方法,建立了地面车辆目标的信号发生的模型。然后,着重讨论地震波的传播、衰减、频散等各方面的影响,通过波动方程定性的分析了传播过程对信号本身特性及其识别带来的影响。然后,分析实际外场环境下可能的背景噪声状况,提出了基于自适应阈值的恒虚警率目标探测方法,在保证探测率的同时,极大程度的降低了系统虚警率。
在此基础上,对地面目标信号进行了特征的提取和优化。分别从信号的时频、频域和时频域分析了信号的基本特性。重点讨论了地面人员特征量的提取,运用自适应阈值小波去噪方法对地面人员信号进行了去噪处理,极大程度的提高了信号的信噪比。地面车辆信号在时频域具有明显的特征,与频域相结合,提出了多特征联合的特征提取方法。根据外场实际采集的目标样本,建立了大规模的目标特征库。
为了应对外界复杂情况,提高识别率,采用了基于统计学习理论和支持向量机的目标识别方法,通过与其它模式识别方法的对比实验,说明支持向量机的分类方法具有更好分类能力和泛化性,在目标识别领域有广阔的应用前景。
在光纤传感器网络中,多传感器协同处理是提高系统整体性能的基本方法,本文采用基于DS证据理论的判决融合方法,提高了系统的分类正确率。在实际外场应用中,多混叠目标的探测与识别问题是亟待研究和解决的问题。本文首次采用独立分量分析的方法对多混叠目标的地震动信号进行了分析,首先将混叠信号进行分离,然后再分别进行识别。建立了多混叠信号的基本模型,运用FastICA算法进行了仿真实验,说明独立成分分析是解决多目标问题的有效算法。
In this paper, the problems about ground target detection, classificationin fiber seismic senor network are discussed. The unattended ground sensor(UGS) system was developed in military for ground target detection, classificationand tracking in battle field surveillance. The fiber seismic senors have a lotof advantages such as high sensitivity, electromagnetic resistance and networkorganizing easiness. The fiber sensor network has been an important complementfor ground sensor network. In the battle field, the movement of ground targetsuch as personnel, tracked vehicle or wheeled vehicle motivates the seismic wavesthat propagating along with the ground surface. The seismic signal received bythe seismic senor can be analyzed for target classification.
First, the generation and propagation of seismic signal caused by movingtarget is discussed. An detection method of personnel footstep is discussed ac-cording to its impulse nature. Then a model of vehicle vibration when movingis built that can be used to analyze the signal characteristic. The propaga-tion model of seismic wave is more complicated to be analyzed. In propagationprocedure, the amplitude of the signal is attenuated, and the frequency has dis-persion. An improved constant false alarm rate (CFAR) detection method basedon adaptive threshold is used to decrease the false alarm in high backgroundnoise. Second, the feature extraction and selection of seismic signal is discussed.The traditional feature extraction methods in time domain, frequency domainand time-frequency domain are discussed, and an united multi-feature method isproposed. The principle component analysis (PCA) is used to reduce the dimen-sion of the feature vector. According to real-life ground target signal acquired inthe field experiment, a large feature data set of di?erent targets is built. Third,the ground target classification method based on statistical learning theory andsupport vector machine is discussed. The traditional classifiers such as Bayes clas-sifier, neural network have some disadvantages when the sample number is small.The comparison experiment shows that the support vector machine (SVM) clas- sifier outperforms the other method, and have bright future in this application.At last, the problems about multi-sensor and multi-target detection and recog-nition are discussed. The decision fusion strategy based on DS evidence theoryis used to improve the overall performance of sensor network. The multi-targetmixed signal processing is a di?cult problem to be tacked in sensor network. Weuse the independent component analysis (ICA) to separate the mixed signal, andthen to classify them separately. The emulation experiment shows that the ICAis an e?ective method.
引文
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