噪声背景下短波莫尔斯信号的自动检测和识别研究
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摘要
本论文研究的是噪声背景下的莫尔斯信号的检测和识别技术。通讯系统中不可避免的要夹杂干扰,对于通过短波信道传输的莫尔斯信号更是如此。这对于从事人工接听莫尔斯信号的工作人员非常不利,它增加了接收人员的听觉和脑力疲劳。利用计算机实现短波信道传输的莫尔斯信号的自动检测和识别就可以减轻相关人员的劳动负担,改善工作环境。
针对莫尔斯信号的周期频率间断性的特点,本文给出了将莫尔斯信号经过短时傅立叶变换后形成的时频谱作为一幅图像、从二维的角度研究一维莫尔斯信号特征的方法。在这种思想的指导下,文中采用了数字图像处理技术,通过理论和实验分析了不同算法的优缺点和对莫尔斯信号检测的适用性。
在对莫尔斯信号谱图的图像增强过程中,主要采用了基于小波的反锐化掩模增强和基于对比度增强的算法;图像的分割主要是采用阈值分割的方法,讨论了全局阈值法和局部阈值法的增强效果,包括基于灰度均值法、基于类间最大方差的自适应阈值法和全局和局部阈值相结合的分割算法等;另外,针对莫尔斯信号谱图的特点引入了数学形态学的处理方法对噪声进一步处理,同时,给出了主要是针对目标形状的特征提取算法:最后,运用统计模式识别和智能纠错相结合的方法实现了莫尔斯信号到文本的转换。
通过理论分析和对实际含噪短波莫尔斯信号的实验,验证了本文中采用的算法的有效性,具有一定的实用价值。
In this thesis the Morse telegraph code detection and recognition in noise background is investigated. It is inevitable that the communication system is disturbed by noises, especially in the short wave communication. This is great disadvantage for people who engaged in receiving signals artificially. Realizing short Morse automatic detection and recognition by computer can lighten relevant people's burden and improve their work environment.On the basis of the essential character of Morse signal's periodicity and frequency discontinuity, a method is put forward that Morse signal's spectrogram through STFT(Short Time Fourier Transform ) is considered as a image and researched from the view of two-dimension. With this idea's guidance, digital image processing technology is adapted and various algorithm's advantage and disadvantage and their applicability for Morse signal detection are analyzed.In image enhancement, antiunsharped marking enhancement and contrast enhancement are adopted; In image segmentation, several algorithm, such as, threshold segmentation based on Arithmetic mean of Gray Value, adaptive threshold segmentation based on maximum between-cluster variance and segmentation based on combination of global and local threshold are discussed. At the same time Mathematical Morphology is introduced to deal with noises and feature abstraction algorithm mainly according to object's shape in Morse signal spectrogram is proposed. Finally, statistical pattern recognition and intelligent correction are applied to achieve Morse signal to texts translation.Theory analysis and experimentation for Morse signal with noises shows that the whole algorithm is effective and feasible.
针对莫尔斯信号的周期频率间断性的特点,本文给出了将莫尔斯信号经过短时傅立叶变换后形成的时频谱作为一幅图像、从二维的角度研究一维莫尔斯信号特征的方法。在这种思想的指导下,文中采用了数字图像处理技术,通过理论和实验分析了不同算法的优缺点和对莫尔斯信号检测的适用性。
在对莫尔斯信号谱图的图像增强过程中,主要采用了基于小波的反锐化掩模增强和基于对比度增强的算法;图像的分割主要是采用阈值分割的方法,讨论了全局阈值法和局部阈值法的增强效果,包括基于灰度均值法、基于类间最大方差的自适应阈值法和全局和局部阈值相结合的分割算法等;另外,针对莫尔斯信号谱图的特点引入了数学形态学的处理方法对噪声进一步处理,同时,给出了主要是针对目标形状的特征提取算法:最后,运用统计模式识别和智能纠错相结合的方法实现了莫尔斯信号到文本的转换。
通过理论分析和对实际含噪短波莫尔斯信号的实验,验证了本文中采用的算法的有效性,具有一定的实用价值。
In this thesis the Morse telegraph code detection and recognition in noise background is investigated. It is inevitable that the communication system is disturbed by noises, especially in the short wave communication. This is great disadvantage for people who engaged in receiving signals artificially. Realizing short Morse automatic detection and recognition by computer can lighten relevant people's burden and improve their work environment.On the basis of the essential character of Morse signal's periodicity and frequency discontinuity, a method is put forward that Morse signal's spectrogram through STFT(Short Time Fourier Transform ) is considered as a image and researched from the view of two-dimension. With this idea's guidance, digital image processing technology is adapted and various algorithm's advantage and disadvantage and their applicability for Morse signal detection are analyzed.In image enhancement, antiunsharped marking enhancement and contrast enhancement are adopted; In image segmentation, several algorithm, such as, threshold segmentation based on Arithmetic mean of Gray Value, adaptive threshold segmentation based on maximum between-cluster variance and segmentation based on combination of global and local threshold are discussed. At the same time Mathematical Morphology is introduced to deal with noises and feature abstraction algorithm mainly according to object's shape in Morse signal spectrogram is proposed. Finally, statistical pattern recognition and intelligent correction are applied to achieve Morse signal to texts translation.Theory analysis and experimentation for Morse signal with noises shows that the whole algorithm is effective and feasible.
引文
[1] 王忠盛.MAD—Ⅰ型莫尔斯电码自动译码机.通信与计算技术.1984,25(4):128-129P
[2] 林瑞华等泽.用微处理机对莫尔斯电码进行机器译码.通信与计算技术》.1983,23(5):130-133P
[3] Online Morse code automatic recognition with neural network. systemAnnual Reports of the Research Reactor Institute, Kyoto Universit, 2001, 1: 684-686P
[4] 于宏毅,张贴等.手工莫尔斯报的一种新型自动收报算法及其手法识别算法.信号处理.1995,11(4):317-320P
[5] 岳喜才,郑崇勋.基于离散Gabor谱的短波电报信号检测.数据采集与处理.1999,14(1):22-25P
[6] 林佳仕.短波通信中信号检测算法研究.哈尔滨工程大学硕士论文.2003:46-50P
[7] 戴逸松.微弱信号检测方法及仪器.北京.国防工业出版社,1994:123-130P
[8] B. Msadler and G. B. Giannakis. Detection in colored non-Gaussian noise using cummlants. in Proc. IEEE ICASSP'93. 1993, 4(5): 204-207P
[9] 申丽然.基于听觉外周模型和高阶统计量语音流检测.哈尔滨工程大学硕士论文,2003:26-30P
[10] 胡航.语音信号处理.第1版.机械工业出版社,2003:102-106P
[11] 易克初.语音信号处理.第1版.国防工业出版社,2000:67-71P
[12] 阮秋琦.数字图像处理学.第2版 电子工业出版社,2001:202-210P
[13] John B. Zimmerman, Stephen M. Pizer, Edward V. Staab. An Evaluation of the Effectiveness of Adaptive Histogram Equalization for Contrast Enhancement. IEEE Trans. on Medical Imaging, 1998, 7(4): 304-312P.
[14] JONG-SEN LEE, Digital Image Enhancement and Noise Filtering by Use of Local Statistics, IEEE Trans. on Pattern Anal. and Machine Intell. 1980, 2(2): 165-168P
[15] 朱菊华,杨新,李俊,施鹏飞.基于纹理分析的保细一种平滑滤波器.中国图形图像学报.2001,6(11):1058-1064P
[16] Richard Alan Peters, A New Algorithm for Image Noise Reduction using Mathematical Morphology. IEEE Trans. on Image Processing 1995, 26(3): 554-568P
[17] Kamel Belkacem-Boussaid and Azeddine Beghdadi, A New Image Smoothing Method Based on a Simple Model of Spatial Pro. in the Early Stages of Human Vision, IEEE Trans. on Image Pro. 2000, 9(2): 220-226P
[18] 朱岩.强噪声下基于小波变换的语音增强研究.哈尔滨工程大学硕士论文,2003:15-16P
[19] Mallat分析,杨力华,戴道清,黄文良等译.第1版,机械工业出版社,2002:122-135P
[20] A. Bakhtazad, A. Palazoglu. Process Data De-noising Using Wavelet Transform Intelligent Data Analysis. 1999, 8(3): 267-285P
[21] Zhang Y J. Objective and quantitative segmentation evaluation and comparison. Signal Processing. 1994, 4(39): 43-45P
[22] 罗希平,田捷,诸葛婴.图像分割方法综述.模式识别与人工智能.1999,4(3):300-312P
[23] Sahoo P K, Soltani S, Wang A K C, Chen Y C. A Survey of Threshold Techniques. Computer Vision. Graphics and Image Processing. 1998, 9(41): 233-260P
[24] 夏良正.数字图像处理.东南大学出版社.修订版.南京,1999:143-150P
[25] Canny J. A Computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence. 1986, 8(6): 679-698P
[26] Koster A S E, et al..Probabilistic Multiscale Image Segmentation. IEEE Trans on PAMI. 1997, 19(2): 109-120P
[27] Kass M, et al..Snakes: Active contour models. International Journal of Computer Vision. 1987, 1(4): 321-331P
[28] Otsu N. A threshold selection method from graylevel histogram[J]. IEEE Trans on System, Man, Cybernetics, 1978, 9(8): 62-66P
[29] 方敏,徐俊艳,王建平等.一种新的文本图像二值化方法[J].合肥工业大学学报(自然科学版).2001,24(2):166-169P
[30] 叶芗芸,戚飞虎,吴健渊等.文本图像的快速二值化方法[J].红外与毫米波学报.1997,16(5):344-350P
[31] 高永英,张利,吴国威.一种基于灰度期望值的图像二值化算法[J].中国图像图形学报.1999,4(6):525-528P
[32] 刘志敏.数学形态学在图像分析中的应用研究.上海交通大学硕士学位论文,1998:26-27P
[33] 景晓军,李剑锋,熊玉庆.静止图像的一种自适应平滑滤波算法.通信学报.2002,23(10):6-14P
[34] 赵春晖著.数字形态滤波器理论及其算法研究.哈尔滨工业大学博士学位论文,1998:30-34P
[35] Gady Agam, Regulated morphological operations, Pattern Recognition, 1999, 32(6): 947-971P
[36] 边肇棋,张学工等.模式识别.第二版.清华大学出版社,2000:132-141P.
[37] 高欣.任意背景下一类矩形目标识别技术研究.西安建筑科技大学硕士论文,2001:25-27P
[38] The C and Chin R. On image analysis by the method of moment, IEEE Trans, Pattem Anal. Machine latell, 1988, 10(4): 291-310P
[39] Pavlidis T. Algoruthms for shape analysis and waveforms. IEEE Trans. Pattern Anal. Machine latell, 1980, 9(2): 301-312P
[40] 朱志刚 数字图像处理.第1版.电子工业出版社,1998:267-269P
[41] A Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York, 3rd edition ,1991
[42] Milan Sonka Vaclav Hlavac.图像处理、分析与机器视觉.第1版.人民邮电出版社,2003:256-259P
[43] 赵荣椿.数字图像处理.第二版.西北工业大学出版社,1996.101-111P
[44] 黄晓凌,廖孟扬.基于小波分析的x射线照片增强研究.武汉大学学报.1998,14(1):121-124P
[2] 林瑞华等泽.用微处理机对莫尔斯电码进行机器译码.通信与计算技术》.1983,23(5):130-133P
[3] Online Morse code automatic recognition with neural network. systemAnnual Reports of the Research Reactor Institute, Kyoto Universit, 2001, 1: 684-686P
[4] 于宏毅,张贴等.手工莫尔斯报的一种新型自动收报算法及其手法识别算法.信号处理.1995,11(4):317-320P
[5] 岳喜才,郑崇勋.基于离散Gabor谱的短波电报信号检测.数据采集与处理.1999,14(1):22-25P
[6] 林佳仕.短波通信中信号检测算法研究.哈尔滨工程大学硕士论文.2003:46-50P
[7] 戴逸松.微弱信号检测方法及仪器.北京.国防工业出版社,1994:123-130P
[8] B. Msadler and G. B. Giannakis. Detection in colored non-Gaussian noise using cummlants. in Proc. IEEE ICASSP'93. 1993, 4(5): 204-207P
[9] 申丽然.基于听觉外周模型和高阶统计量语音流检测.哈尔滨工程大学硕士论文,2003:26-30P
[10] 胡航.语音信号处理.第1版.机械工业出版社,2003:102-106P
[11] 易克初.语音信号处理.第1版.国防工业出版社,2000:67-71P
[12] 阮秋琦.数字图像处理学.第2版 电子工业出版社,2001:202-210P
[13] John B. Zimmerman, Stephen M. Pizer, Edward V. Staab. An Evaluation of the Effectiveness of Adaptive Histogram Equalization for Contrast Enhancement. IEEE Trans. on Medical Imaging, 1998, 7(4): 304-312P.
[14] JONG-SEN LEE, Digital Image Enhancement and Noise Filtering by Use of Local Statistics, IEEE Trans. on Pattern Anal. and Machine Intell. 1980, 2(2): 165-168P
[15] 朱菊华,杨新,李俊,施鹏飞.基于纹理分析的保细一种平滑滤波器.中国图形图像学报.2001,6(11):1058-1064P
[16] Richard Alan Peters, A New Algorithm for Image Noise Reduction using Mathematical Morphology. IEEE Trans. on Image Processing 1995, 26(3): 554-568P
[17] Kamel Belkacem-Boussaid and Azeddine Beghdadi, A New Image Smoothing Method Based on a Simple Model of Spatial Pro. in the Early Stages of Human Vision, IEEE Trans. on Image Pro. 2000, 9(2): 220-226P
[18] 朱岩.强噪声下基于小波变换的语音增强研究.哈尔滨工程大学硕士论文,2003:15-16P
[19] Mallat分析,杨力华,戴道清,黄文良等译.第1版,机械工业出版社,2002:122-135P
[20] A. Bakhtazad, A. Palazoglu. Process Data De-noising Using Wavelet Transform Intelligent Data Analysis. 1999, 8(3): 267-285P
[21] Zhang Y J. Objective and quantitative segmentation evaluation and comparison. Signal Processing. 1994, 4(39): 43-45P
[22] 罗希平,田捷,诸葛婴.图像分割方法综述.模式识别与人工智能.1999,4(3):300-312P
[23] Sahoo P K, Soltani S, Wang A K C, Chen Y C. A Survey of Threshold Techniques. Computer Vision. Graphics and Image Processing. 1998, 9(41): 233-260P
[24] 夏良正.数字图像处理.东南大学出版社.修订版.南京,1999:143-150P
[25] Canny J. A Computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence. 1986, 8(6): 679-698P
[26] Koster A S E, et al..Probabilistic Multiscale Image Segmentation. IEEE Trans on PAMI. 1997, 19(2): 109-120P
[27] Kass M, et al..Snakes: Active contour models. International Journal of Computer Vision. 1987, 1(4): 321-331P
[28] Otsu N. A threshold selection method from graylevel histogram[J]. IEEE Trans on System, Man, Cybernetics, 1978, 9(8): 62-66P
[29] 方敏,徐俊艳,王建平等.一种新的文本图像二值化方法[J].合肥工业大学学报(自然科学版).2001,24(2):166-169P
[30] 叶芗芸,戚飞虎,吴健渊等.文本图像的快速二值化方法[J].红外与毫米波学报.1997,16(5):344-350P
[31] 高永英,张利,吴国威.一种基于灰度期望值的图像二值化算法[J].中国图像图形学报.1999,4(6):525-528P
[32] 刘志敏.数学形态学在图像分析中的应用研究.上海交通大学硕士学位论文,1998:26-27P
[33] 景晓军,李剑锋,熊玉庆.静止图像的一种自适应平滑滤波算法.通信学报.2002,23(10):6-14P
[34] 赵春晖著.数字形态滤波器理论及其算法研究.哈尔滨工业大学博士学位论文,1998:30-34P
[35] Gady Agam, Regulated morphological operations, Pattern Recognition, 1999, 32(6): 947-971P
[36] 边肇棋,张学工等.模式识别.第二版.清华大学出版社,2000:132-141P.
[37] 高欣.任意背景下一类矩形目标识别技术研究.西安建筑科技大学硕士论文,2001:25-27P
[38] The C and Chin R. On image analysis by the method of moment, IEEE Trans, Pattem Anal. Machine latell, 1988, 10(4): 291-310P
[39] Pavlidis T. Algoruthms for shape analysis and waveforms. IEEE Trans. Pattern Anal. Machine latell, 1980, 9(2): 301-312P
[40] 朱志刚 数字图像处理.第1版.电子工业出版社,1998:267-269P
[41] A Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York, 3rd edition ,1991
[42] Milan Sonka Vaclav Hlavac.图像处理、分析与机器视觉.第1版.人民邮电出版社,2003:256-259P
[43] 赵荣椿.数字图像处理.第二版.西北工业大学出版社,1996.101-111P
[44] 黄晓凌,廖孟扬.基于小波分析的x射线照片增强研究.武汉大学学报.1998,14(1):121-124P