基于微摄动与步态特征的人体探测算法研究
发布时间：2019-06-17 17:38
【摘要】：基于微波雷达的人体探测主要是利用人体运动形成的多普勒效应,实现对人体的检测、定位和跟踪,在现代城市战争、自然灾害救援、医疗监护等领域具有重要的应用价值。由于实际探测中场景复杂,人体微摄动以及步态信号回波形式多样、能量微弱,造成难以有效检测。本文围绕穿墙场景下人体检测难题开展研究,主要工作和贡献如下:1.针对穿墙场景下的人体探测难题,研究了单频连续波雷达体制和步进频连续波雷达体制下的人体回波信号模型,并对人体回波信号进行分析。通过仿真确定了穿墙人体探测情况下雷达发射信号的最佳频段;2.针对低信杂比、复杂非高斯杂波场景下人体微摄动信号稳健检测难题,提出了双通道相关熵探测算法,该算法首先通过计算回波信号的相关熵,实现对杂波的抑制以及人体微摄动信号能量的积累,然后对相关熵做傅里叶变换,得到相关熵谱,从而实现人体探测。通过与传统人体探测算法效果的对比,证明了该算法能够有效提高探测结果信噪比;3.针对人体微摄动信号回波能量弱,单幅回波图像包含信息量少的情况,研究了利用双幅图像进行相关分析的人体探测算法,该算法通过目标与场景的相关分析,有效加强了微弱目标信号能量,有利于从强杂波中提取人体微弱信息。通过仿真验证了该算法的有效性,并与传统人体探测算法进行了效果上的对比;4.针对人体步态回波信号具有时变性强,包含频率分量多的情况,将时频分析方法用于人体步态回波信号分析,首先对人体步态回波进行短时傅里叶变换,得到人体步态运动回波时频图,然后对二值化处理,提取上下包络,最后对谱图中的不同参数进行分析,并对从谱图中提取步态特征参数的方法进行了总结;5.针对运动状态的人体回波形式多样的情况,进行了将支持向量机分类方法用于人体运动状态识别领域的研究。将人体步态谱图中代表不同物理意义的特征参数值作为支持向量机的输入,从而实现人体不同运动状态的分类。
[Abstract]:The human body detection based on the microwave radar is mainly the Doppler effect formed by the movement of the human body, realizes the detection, positioning and tracking of the human body, and has important application value in the fields of modern city war, natural disaster rescue, medical monitoring and the like. Due to the complex scene, the micro-perturbation of the human body and the various forms of the gait signal echo, the energy is weak, which makes it difficult to detect effectively. This paper studies the problem of human body detection under the wall-wall scene, and the main work and contribution are as follows:1. The human body echo signal model under the system of single-frequency continuous wave radar and the step-frequency continuous wave radar system is studied in the light of the human body detection problem under the through-wall scene, and the human body echo signal is analyzed. The best frequency range of radar emission signal under the condition of through-wall human detection is determined by simulation. in ord to solve that problem of robust detection of the micro-perturbation signal in a complex non-Gaussian clutter scene for low signal-to-noise ratio and complex non-Gaussian clutter, a two-channel correlation entropy detection algorithm is proposed, which first achieves the suppression of clutter and the accumulation of the energy of the micro-perturbation signal of the human body by calculating the relative entropy of the echo signal, Then carrying out Fourier transform on the related entropy to obtain the relevant entropy spectrum, so as to realize human body detection. Compared with the traditional human body detection algorithm, it is proved that the algorithm can effectively improve the signal-to-noise ratio of the detection result. Aiming at the weak echo energy of the micro-perturbation signal of the human body, the single-amplitude echo image contains a small amount of information, the human body detection algorithm using the double-amplitude image to carry out correlation analysis is researched, the algorithm can effectively enhance the energy of the weak target signal through the correlation analysis of the target and the scene, And the weak information of the human body can be extracted from the strong clutter. The effectiveness of the algorithm is verified by simulation, and compared with the traditional human body detection algorithm. the time-frequency analysis method is used for analyzing the gait echo signals of a human body, The upper and lower envelope is extracted, and the different parameters in the spectrum are analyzed, and the method for extracting the gait characteristic parameters from the spectrogram is summarized. In this paper, the support vector machine classification method is applied to the field of human motion state identification. And the characteristic parameter values representing different physical meanings in the human gait spectrum map are used as input of the support vector machine so as to realize the classification of different motion states of the human body.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51
本文编号：2501151
[Abstract]:The human body detection based on the microwave radar is mainly the Doppler effect formed by the movement of the human body, realizes the detection, positioning and tracking of the human body, and has important application value in the fields of modern city war, natural disaster rescue, medical monitoring and the like. Due to the complex scene, the micro-perturbation of the human body and the various forms of the gait signal echo, the energy is weak, which makes it difficult to detect effectively. This paper studies the problem of human body detection under the wall-wall scene, and the main work and contribution are as follows:1. The human body echo signal model under the system of single-frequency continuous wave radar and the step-frequency continuous wave radar system is studied in the light of the human body detection problem under the through-wall scene, and the human body echo signal is analyzed. The best frequency range of radar emission signal under the condition of through-wall human detection is determined by simulation. in ord to solve that problem of robust detection of the micro-perturbation signal in a complex non-Gaussian clutter scene for low signal-to-noise ratio and complex non-Gaussian clutter, a two-channel correlation entropy detection algorithm is proposed, which first achieves the suppression of clutter and the accumulation of the energy of the micro-perturbation signal of the human body by calculating the relative entropy of the echo signal, Then carrying out Fourier transform on the related entropy to obtain the relevant entropy spectrum, so as to realize human body detection. Compared with the traditional human body detection algorithm, it is proved that the algorithm can effectively improve the signal-to-noise ratio of the detection result. Aiming at the weak echo energy of the micro-perturbation signal of the human body, the single-amplitude echo image contains a small amount of information, the human body detection algorithm using the double-amplitude image to carry out correlation analysis is researched, the algorithm can effectively enhance the energy of the weak target signal through the correlation analysis of the target and the scene, And the weak information of the human body can be extracted from the strong clutter. The effectiveness of the algorithm is verified by simulation, and compared with the traditional human body detection algorithm. the time-frequency analysis method is used for analyzing the gait echo signals of a human body, The upper and lower envelope is extracted, and the different parameters in the spectrum are analyzed, and the method for extracting the gait characteristic parameters from the spectrogram is summarized. In this paper, the support vector machine classification method is applied to the field of human motion state identification. And the characteristic parameter values representing different physical meanings in the human gait spectrum map are used as input of the support vector machine so as to realize the classification of different motion states of the human body.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51
【参考文献】
相关期刊论文 前2条
1 姚晓波,刘泉;小波变换与中值滤波耦合的雷达信号去噪法[J];武汉理工大学学报;2005年02期
2 张翼;邱兆坤;朱玉鹏;黎湘;;基于微多普勒特征的人体步态参数估计[J];信号处理;2010年06期
，本文编号：2501151
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