生命探测雷达信号处理算法研究
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摘要
本文主要针对自主研制开发的生命探测雷达进行信号处理的算法研究，生命特征信号属于低速运动目标信号，它所产生的多普勒频移非常小，微弱的回波信号极易淹没在强杂波背景下。如何更加有效准确地检测和提取出所需的微弱目标信号即生命特征信号，是本文研究的重点。
接收到的生命特征信号是频率极低、准周期、低信噪比、多谐波组合的信号。采样数据的分析表明：杂波符合高斯分布；生命特征信号可以简化为谐波模型来进行信号提取、杂波抑制和提高信噪比的处理。
论文对生命探测雷达采集的生命特征信号数据作了大量的仿真和实验，提出了基于传统小波变换的Mallat算法，利用多分辨率特性求得一镜像滤波器组，选择具有较好正则性和光滑性的Symlets小波系来进行低频生命特征信号的提取。仿真结果表明，小波变换算法能够有效的准确的提取出生命特征信号。
生命探测雷达的数字信号处理算法建立在以上仿真结果基础上，在C语言环境下完成的。主要分成时域处理和频域处理两大部分。
最后，论文比较了传统小波变换与FIR低通滤波方法应用于低速目标信号检测的结果，比较得出传统小波变换要比FIR低通滤波器方法对信号检测的群延迟要小的多，并且可以得到更高的信噪比。合理的将多种信号处理技术相结合的方法能更加有效、准确地检测并提取出低速目标信号，在工程中应用前景更加广泛。
The signal processing for the life detector developed independently was discussed in the paper. Life signals are low-velocity target signals with very small Doppler frequency shifts and very weak echo signals easily submerged in clutter noise. It is a keystone to detect and pick up LVT more availably and veraciously in this paper.
The collected life signals can be built as harmonic modeling with their low-frequency, semi-periods, low SNR and Gauss-distributed clutter. Based on the characters above, extracting signals, restraining clutters and improving SNR can be done.
Lots of simulations and experiments were made out of the sampled life signals. The method of Mallat of Wavelet-transform was introduced, which utilize the capability of Multi-resolving to obtain a mirror filter group, then choose a Symlets wavelet system to extract the parameters of the life signals. The result of simulation shows that the method of Wavelet-transform can pick up Life-signals efficiently.
The arithmetic of digital signal processing was based on the simulation above, and completed with C language. It is mostly divided into two parts: Time processing and Frequency processing.
Finally, the comparison with the result of Wavelet-transform and the FIR filter at detecting low-velocity target shows that the delay of Wavelet-transform is shorter than that of FIR and SNR is improved. Utilizing various signal processing methods suitably can extract the target signals more availably and exactly, which make it more extensive in the future.
接收到的生命特征信号是频率极低、准周期、低信噪比、多谐波组合的信号。采样数据的分析表明：杂波符合高斯分布；生命特征信号可以简化为谐波模型来进行信号提取、杂波抑制和提高信噪比的处理。
论文对生命探测雷达采集的生命特征信号数据作了大量的仿真和实验，提出了基于传统小波变换的Mallat算法，利用多分辨率特性求得一镜像滤波器组，选择具有较好正则性和光滑性的Symlets小波系来进行低频生命特征信号的提取。仿真结果表明，小波变换算法能够有效的准确的提取出生命特征信号。
生命探测雷达的数字信号处理算法建立在以上仿真结果基础上，在C语言环境下完成的。主要分成时域处理和频域处理两大部分。
最后，论文比较了传统小波变换与FIR低通滤波方法应用于低速目标信号检测的结果，比较得出传统小波变换要比FIR低通滤波器方法对信号检测的群延迟要小的多，并且可以得到更高的信噪比。合理的将多种信号处理技术相结合的方法能更加有效、准确地检测并提取出低速目标信号，在工程中应用前景更加广泛。
The signal processing for the life detector developed independently was discussed in the paper. Life signals are low-velocity target signals with very small Doppler frequency shifts and very weak echo signals easily submerged in clutter noise. It is a keystone to detect and pick up LVT more availably and veraciously in this paper.
The collected life signals can be built as harmonic modeling with their low-frequency, semi-periods, low SNR and Gauss-distributed clutter. Based on the characters above, extracting signals, restraining clutters and improving SNR can be done.
Lots of simulations and experiments were made out of the sampled life signals. The method of Mallat of Wavelet-transform was introduced, which utilize the capability of Multi-resolving to obtain a mirror filter group, then choose a Symlets wavelet system to extract the parameters of the life signals. The result of simulation shows that the method of Wavelet-transform can pick up Life-signals efficiently.
The arithmetic of digital signal processing was based on the simulation above, and completed with C language. It is mostly divided into two parts: Time processing and Frequency processing.
Finally, the comparison with the result of Wavelet-transform and the FIR filter at detecting low-velocity target shows that the delay of Wavelet-transform is shorter than that of FIR and SNR is improved. Utilizing various signal processing methods suitably can extract the target signals more availably and exactly, which make it more extensive in the future.
引文
[1] 丁鹭飞 耿富录． 雷达原理(修订版)．西安：西安电子科技大学出版社，1984
[2] 程佩青．数字信号处理．北京：清华大学出版社，1995
[3] 张贤达．现代信号处理．北京：清华大学出版社，2002
[4] 倪安胜．伤员探测中生命体识别技术研究．第四军医大学硕士学位论文．2003，5
[5] 赵树杰，史林．数字信号处理．西安电子科技大学出版社．1997
[6] 付丽琴等．数字信号处理原理及实现．北京：国防工业出版社，2004
[7] 楼顺天等．基于MATLAB的系统分析和设计——信号处理．西安电子科技大学出版社．1999
[8] 胡昌华等．基于MATLAB的系统分析与设计—时频分析．西安：西安电子科技大学出版社 2002
[9] 杨福生等．小波变换的工程分析与应用．北京：科学出版社 2000
[10] 秦前清，杨宗凯 实用小波分析 西安：西安电子科技大学出版社 1994
[11] Albert Boggess，Francis J．Narcowich著，芮国胜，康健等译 小波与傅里叶分析基础 北京：电子工业出版社 2004
[12] 宗孔德 多抽样率信号处理 清华大学出版社 1996
[13] Vinay K．Ingle，John G．Proakis著，陈怀琛等译 数字信号处理及其MATLAB实现 北京：电子工业出版社 1998
[14] 董长虹主编，高志，余啸海编著 Matlab小波分析工具箱原理与应用 北京：国防工业出版社 2004
[15] 程正兴 小波分析算法与应用西 安西 安交通大学出版社 1998
[16] 张明有 吕明编著．近代信号处理理论与方法．北京：国防工业出版社 2005
[17] 薛年喜主编．MATLAB在数字信号处理中的应用 北京：清华大学出版社 2003
[18] 郭山红，孙锦涛等．穿墙生命探测技术研究．南京理工大学学报．Vol 29．No．2．Apr．2005．p186-171
[19] 史林，姜敏等．基于谐波模型的生命探测雷达人体状态识别方法．西安电子科技大学学报．Vol 32．No．2．Apr．2005．p180-183
[20] 崔江涛，孙君顶等．基于小波变换的多分辨高维图像检索方法．西安电子科技学报．Vol 32．No．3．Jun．2005．p371-374
[21] 韩冰，姬红兵等．一种基于小波的分层和多分辨的镜头边界检测方法．西安电子科技大学学报．Vol 32．No．1．Feb．2005．p36-40
[22] 保宏，段宝岩等．基于小波的非线性系统在线状态估计．西安电子大学学报． Vol 31.No.5.Oct.2004.p693-700
[23] Mallat Stephen. Singularity Detection and Processing with wavelets. IEEE Trans Information Theory. 1992. p113-132
[24] [美]E．F．Greneker 雷达远距离探测心跳和呼吸的技术及应用 雷达与电子战 1998(1)：p23-p27
[25] Mehrdad Soumekh. Signal processing of wide bandwidth and wide beamwidth P-3 SAR data. IEEE Transactions on Volume 37, Issue 4, Oct. 2001. P1122-1141
[26] Amy D. Droitcour. Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring. Microwave Theory and Techniques, IEEE Transactions onVolume 52, Issue 3, March 2004. p838-848
[27] Frazier, L. M. MDR for law enforcement. Potentials, IEEE Volume 16, Issue 5, Dec 1997-Jan 1998 p23-26
[28] Sweldens W. and Schroder P. Building your own wavelets at home Wavelets in Computer Graphics, ACM SIGGRAPH Course Notes, 1996, p15-87.
[29] Daubechiess I. And Sweldens W. Wavelet families of increading order in arbitrary dimensions. IEEE Trans. On Image Processing, 2003.3, 9(3): p480-496
[30] WeiBocheng Euclidean geometry of curved exponential families and its application to confidence regions. Appl. Math. JCU 9B(1994), p375-382
[31] "ADS P-TS101S datasheet" Analog Devicess Inc. 2002
[32] "ADS P-TS101S Hardware manual" Analog Devicess Inc. 2002
[33] "ADSP-TS101S Instruction Set Specification" Analog Devicess Inc. 2002
[2] 程佩青．数字信号处理．北京：清华大学出版社，1995
[3] 张贤达．现代信号处理．北京：清华大学出版社，2002
[4] 倪安胜．伤员探测中生命体识别技术研究．第四军医大学硕士学位论文．2003，5
[5] 赵树杰，史林．数字信号处理．西安电子科技大学出版社．1997
[6] 付丽琴等．数字信号处理原理及实现．北京：国防工业出版社，2004
[7] 楼顺天等．基于MATLAB的系统分析和设计——信号处理．西安电子科技大学出版社．1999
[8] 胡昌华等．基于MATLAB的系统分析与设计—时频分析．西安：西安电子科技大学出版社 2002
[9] 杨福生等．小波变换的工程分析与应用．北京：科学出版社 2000
[10] 秦前清，杨宗凯 实用小波分析 西安：西安电子科技大学出版社 1994
[11] Albert Boggess，Francis J．Narcowich著，芮国胜，康健等译 小波与傅里叶分析基础 北京：电子工业出版社 2004
[12] 宗孔德 多抽样率信号处理 清华大学出版社 1996
[13] Vinay K．Ingle，John G．Proakis著，陈怀琛等译 数字信号处理及其MATLAB实现 北京：电子工业出版社 1998
[14] 董长虹主编，高志，余啸海编著 Matlab小波分析工具箱原理与应用 北京：国防工业出版社 2004
[15] 程正兴 小波分析算法与应用西 安西 安交通大学出版社 1998
[16] 张明有 吕明编著．近代信号处理理论与方法．北京：国防工业出版社 2005
[17] 薛年喜主编．MATLAB在数字信号处理中的应用 北京：清华大学出版社 2003
[18] 郭山红，孙锦涛等．穿墙生命探测技术研究．南京理工大学学报．Vol 29．No．2．Apr．2005．p186-171
[19] 史林，姜敏等．基于谐波模型的生命探测雷达人体状态识别方法．西安电子科技大学学报．Vol 32．No．2．Apr．2005．p180-183
[20] 崔江涛，孙君顶等．基于小波变换的多分辨高维图像检索方法．西安电子科技学报．Vol 32．No．3．Jun．2005．p371-374
[21] 韩冰，姬红兵等．一种基于小波的分层和多分辨的镜头边界检测方法．西安电子科技大学学报．Vol 32．No．1．Feb．2005．p36-40
[22] 保宏，段宝岩等．基于小波的非线性系统在线状态估计．西安电子大学学报． Vol 31.No.5.Oct.2004.p693-700
[23] Mallat Stephen. Singularity Detection and Processing with wavelets. IEEE Trans Information Theory. 1992. p113-132
[24] [美]E．F．Greneker 雷达远距离探测心跳和呼吸的技术及应用 雷达与电子战 1998(1)：p23-p27
[25] Mehrdad Soumekh. Signal processing of wide bandwidth and wide beamwidth P-3 SAR data. IEEE Transactions on Volume 37, Issue 4, Oct. 2001. P1122-1141
[26] Amy D. Droitcour. Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring. Microwave Theory and Techniques, IEEE Transactions onVolume 52, Issue 3, March 2004. p838-848
[27] Frazier, L. M. MDR for law enforcement. Potentials, IEEE Volume 16, Issue 5, Dec 1997-Jan 1998 p23-26
[28] Sweldens W. and Schroder P. Building your own wavelets at home Wavelets in Computer Graphics, ACM SIGGRAPH Course Notes, 1996, p15-87.
[29] Daubechiess I. And Sweldens W. Wavelet families of increading order in arbitrary dimensions. IEEE Trans. On Image Processing, 2003.3, 9(3): p480-496
[30] WeiBocheng Euclidean geometry of curved exponential families and its application to confidence regions. Appl. Math. JCU 9B(1994), p375-382
[31] "ADS P-TS101S datasheet" Analog Devicess Inc. 2002
[32] "ADS P-TS101S Hardware manual" Analog Devicess Inc. 2002
[33] "ADSP-TS101S Instruction Set Specification" Analog Devicess Inc. 2002