基于小波神经网络的多相码雷达信号的旁瓣抑制
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摘要
脉冲压缩技术在现代雷达中应用广泛,它有效地解决了雷达距离分辨力与平均功率之间的矛盾。相位编码信号是常用的脉压信号,随着数字技术和DSP技术的发展,人们开始寻求具有更大的脉冲压缩比的信号,使多相编码信号得到了人们的广泛关注。然而,距离旁瓣限制了相位编码信号脉冲压缩的实际应用。因此,如何更好的抑制距离旁瓣成为相位编码信号脉冲压缩实际应用的关键问题。
本文主要研究和分析了一种基于小波神经网络的多相码雷达信号的旁瓣抑制方法。首先从模糊函数和回波特性的角度介绍了多相编码信号的一些相关特性,并介绍了Taylor码、Frank码、P3、P4码等多相编码信号;其次介绍了多相编码信号的旁瓣抑制方法,本文主要介绍了比较常用的两种方法:直接窗函数加权方法和最小二乘失配滤波方法,并结合其原理进行了多相编码信号旁瓣抑制的仿真特性测试;再次,研究了小波分析理论和小波神经网络的学习方法,重点研究了基于BP算法的小波神经网络并提出了几种BP的算法的改进算法;最后,将改进的BP网络应用到多相编码信号的旁瓣抑制中来,并对其抗噪声性、多普勒容限性和分辨力能力进行了相关仿真试验。试验结果表明,这种小波神经网络旁瓣抑制方法具有较直接窗函数加权法和最小二乘失配滤波法更强的旁瓣抑制能力和更好的多普勒容限性。
The pulse compression is an advanced technique widely applied in the modern Radar. It has effectively solved the contradiction between the radar range resolution and the average power. The phase coded singals are commonly used in pulse compression. And with the development of the digital and DSP technology, people began to seek the signal with greater pulse compression radio, therefore the polyphase coded signal received wide attention. However, their range sidelobes are too high and inadequate for most applications. So, how to minimize the peak range sidelobe of a phase coded waveform is a key to practical applications of phase coded pulse compression.
This article mainly introduced and has analyzed a method of sidelobes suppression for radar polyphase code signal using wavelet neural network. First, introduce some characteristic of polyphase coded signal such as ambiguity function and echo, and introduced some polyphase coded such as Taylor code, frank code, P3, P4 code and so on. Second, intrduced two common used method of sidelobes suppression for polyphase coded singal, windowing weighted sidelobe suppression technology and least squares mismatched filtering algorithm. Combined with the principle of the two methods, we carried on the corresponding simulation. Third, we study the theory of the wavelet and the learning methed of the wavelet neural network, focusing on BP algorithm besed on the wavelet neural network and put forward several improved BP algorithm. Last, using the improved BP network for suppressing sidelobes of the polyphase coded signal, and test its resistance to noise, Doppler tolerance and ability of resolution. The result show that the wavelet network sidelobes suppression method has better characteristic of sidelobe suppression and Doppler tolerance than windowing weighted sidelobe suppression technology and iterative least squares mismatched filtering algorithm.
本文主要研究和分析了一种基于小波神经网络的多相码雷达信号的旁瓣抑制方法。首先从模糊函数和回波特性的角度介绍了多相编码信号的一些相关特性,并介绍了Taylor码、Frank码、P3、P4码等多相编码信号;其次介绍了多相编码信号的旁瓣抑制方法,本文主要介绍了比较常用的两种方法:直接窗函数加权方法和最小二乘失配滤波方法,并结合其原理进行了多相编码信号旁瓣抑制的仿真特性测试;再次,研究了小波分析理论和小波神经网络的学习方法,重点研究了基于BP算法的小波神经网络并提出了几种BP的算法的改进算法;最后,将改进的BP网络应用到多相编码信号的旁瓣抑制中来,并对其抗噪声性、多普勒容限性和分辨力能力进行了相关仿真试验。试验结果表明,这种小波神经网络旁瓣抑制方法具有较直接窗函数加权法和最小二乘失配滤波法更强的旁瓣抑制能力和更好的多普勒容限性。
The pulse compression is an advanced technique widely applied in the modern Radar. It has effectively solved the contradiction between the radar range resolution and the average power. The phase coded singals are commonly used in pulse compression. And with the development of the digital and DSP technology, people began to seek the signal with greater pulse compression radio, therefore the polyphase coded signal received wide attention. However, their range sidelobes are too high and inadequate for most applications. So, how to minimize the peak range sidelobe of a phase coded waveform is a key to practical applications of phase coded pulse compression.
This article mainly introduced and has analyzed a method of sidelobes suppression for radar polyphase code signal using wavelet neural network. First, introduce some characteristic of polyphase coded signal such as ambiguity function and echo, and introduced some polyphase coded such as Taylor code, frank code, P3, P4 code and so on. Second, intrduced two common used method of sidelobes suppression for polyphase coded singal, windowing weighted sidelobe suppression technology and least squares mismatched filtering algorithm. Combined with the principle of the two methods, we carried on the corresponding simulation. Third, we study the theory of the wavelet and the learning methed of the wavelet neural network, focusing on BP algorithm besed on the wavelet neural network and put forward several improved BP algorithm. Last, using the improved BP network for suppressing sidelobes of the polyphase coded signal, and test its resistance to noise, Doppler tolerance and ability of resolution. The result show that the wavelet network sidelobes suppression method has better characteristic of sidelobe suppression and Doppler tolerance than windowing weighted sidelobe suppression technology and iterative least squares mismatched filtering algorithm.
引文
1 M. H. Ackroyd. Synthesis of Efficient Huffman Sequences. IEEE Trans. Aerospace and Electronic Systems. January 1972. Vol. AES-8:P2-8
2 A. M. Kerdock, R. Mayer and D. Bass. Longest Binary pulse Compression Codes with Given Peak Sidelobe Levels. Proeeeding of the IEEE. February 1986. Vol. 74, No.2: P366
3 M. N. Cohen, J. M. Baden, and P. E. Cohen. Biphase Codes with Minimum Peak Sidelobes. IEEE international Radar Conference. 1989: P62-66
4武文,李江等.一种低截获概率雷达信号设计与仿真[A]. CACIS. 2007.
5陈雷,汪立森,潘明海.基于DDS的低截获概率雷达信号设计实现[A]. 2008通信理论与技术新发展第十三届全国青年通信学术会议论文集(下)[C], 2008
6徐健.多相编码脉冲压缩雷达信号的优化设计与仿真.河南大学硕士学位论文. 2007.
7 E. L. Key, E. N. Fowle, and R. D. Haggarty. A Method of Sidelobe Suppression in Phase Coded Pulse Compression Systems. M. I. T. Lincoin Lab. Lexington. Tech. Rept. 209. Nov. 1959
8 A. W. Rihaczek and R. M. Golden. Range Sidelobe Suppression for Barker Codes. IEEE Trans. Aerosp. Electron. Syst. Nov. 1971. Vol. AES-7: P1087~1092
9 M. H. Ackroyd, F. Ghani. Optimum Mismatched Filters for Sidelobe Suppression. IEEE AES. Vol. Aes-9, No. 2, March 1973
10 E. D. Mese and D. Giuli. Optimal Recursive Processing in Phase-Coded Waveform Radars, IEEE Transaction on Aerospace and Electronic Systems, March 1977, vol. AES-13, PP: 163-171.
11 S. Zoraster. Minimum Peak Range Sidelobe Filters for Binary Phase-Coded Waveforms. IEEE. AES. Vol. AES-16, No. l. January 1980
12 J. M. Baden and M. N. Cohen. Optimal Peak Sidelobe Filters for Biphase Pulse Compression. IEEE International Radar Conference. 1990: P249-252
13 S. C. Park and J. F. Doherty. A Minimax Optimization Approach to Sidelobe Suppression Filter Design. IEEE Interational Radar Conference. 1996: P3113-3116.
14 F. F. Kretschmer, L. R.Welch. Sidelobe Reduction Techniques for Polyphase Pulse Compression Codes. IEEE International Radar Conference 2000
15 M. Luszczyk, D. Mucha. Kaiser-Bessel Window Weighting Function for Polyphase Pulse Compression Code. Microwaves, Radar and Wireless Communications, 2008. MIKON 2008. 17th internation Conference on. P1-4
16王丽萍,苏涛.一种迭代加权最小二乘旁瓣抑制滤波器设计[J].火控雷达技术, 2008, 37(1): 92-96.
17 H. K. Kwan and C. K. Lee. A Neural Network Approach to Pulse Radar Detection. IEEE Transaction on Aerospace and Electronic Systems, January 1993, vol. AES-29, PP9-21.
18 H. K. Kwan and C. K. Lee. Pulse Radar Detection Using a Multi-layer Neural Network. IEEE International Joint Conference on Neural Network, 1989, vol. 2, PP75-80.
19 H. K. Kwan and P. C. Tsang. Systolic Implementation of Counterpropagation Networks. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1990, PP953-956.
20 H. K. Kwan. Systolic Architectures for Hopfield network, Bam and Multi-Layer Feed-Forward Network. ISCAS, 1989, PP790-793.
21 K.Deergha Rao and G. Sridhar. Improving Performance in Pulse Radar Detection Using Neural Networks. IEEE Transaction on Aerospace and Electronic Systems, July 1995, vol. AES-31, PP1193-1198.
22 A. J. Zejak, J. A. Zatkalik and M. L. Dukic. Doppler Optimized Compression Fiters. IEEE ISSSTA’94, PP. 534-538, July 1994.
23 I. W. Guest. Application of Neural Nets to Pulse Compression in the Presence of Doppler. Communications and Signal Proeessing 1993 Proceeding of the 1993 IEEE South Afriean Symposium., PP213-217.
24刘源,许荣庆,邓维波,位寅生.基于神经网络的随机跳频信号旁瓣抑制算法.系统工程与电子技术. 2004, 26(8):1124~1128
25杨斌,武剑辉,向敬成.非线性调频信号时域副瓣抑制滤波器设计.信号处理. 1999, (15): 31~351
26位寅生,刘永坦,随机断续高频雷达波形设计和处理.电子学报. 2002, 30(3): 437~440
27李国平.雷达脉冲压缩中相位编码信号旁瓣抑制的研究.大连理工大学硕士学位论文. 2000.
28 Taylor J W, Blinchikoff H J. Quadriphase Code-a Radar Pulse Compression Signal with Unique Characteristics [J]. IEEE Trans On AES, 1988, 24(3): 156-170.
29黄仁业.脉冲压缩信号数字处理技术的研究与实现.西南工学院学报.1999,14(1):24~28
30 Chi-chang Wang, Hsuen-Chyun Shyu. An Extended Frank Code and New Technique for Implementing P3 and P4 Codes. IEEE Trans.on Aerospace and Electronic Systems. 1989, 25(4): 442~448
31 M. Burgos-Garcia, G. Ferreiro-Collar, A. A. Lopez. Digital Monopulse Receivers for Phase Modulated Signals. IEEE International Radar Conference. 2003: 640~645
32 Cz. J. Lesnik, T. W. Brenner, W. J. Kocanda. Efficient Matched Filtering of Signal with Polyphase Frank Coded Sequences. IEEE International Conference on Microwaves and Radar. 1998: 815-819, vol. 3
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34张艳艳.雷达相位编码信号的研究和应用.西安:西安电子科技大学硕士论文. 2007.
35蔡凤丽.相位编码信号脉冲压缩旁瓣抑制技术研究.南京理工大学硕士学位论文. 2009.
36 K. R. Griep, J. A. Ritcey, J. J. Burlingame. Poly-Phase Codes and Optimal Filters for Multiple User Ranging. IEEE Transactions on ASE. Vol. 31, No. 2. April 1995.
37 Qinghua Zhang, el al. Wavelet Networks [J]. IEEE Trans. on Neural Networks. 1992, 3(6):889~898.
38 Pati Y C, Krishnaprasad P. S. Analysis and Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations [J]. IEEE Trans.On Neural Networks.1993, 4(1):73～85
39 Bakshi B R and Stephanopoulos G. Wavelets as Basis Functions for Localized Learning in A Multi-resolution Hierarchy [J]. IJCNN.1992, 140-145 vol.2
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41刘景艳,郭顺京,李玉东.基于模糊小波神经网络的提升机恒减速制动系统的研究[J].工矿自动化, 2010, (06) :40-43
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45郭旭航.小波神经网络在电力谐波检测中的应用[J].科学技术与工程, 2010, (11) :2784-2788
46宋清昆,王建双,王慕坤.基于遗传算法的小波神经网络控制器设计[J].电机与控制学报, 2010, (04) :102-106
47宋弘.小波神经网络在语音识别系统中的应用[J].微电子学与计算机, 2010, (04) :122-124,128
48邓宇.基于小波神经网络的重庆市某主城区饮用水源水水质参数预测模型研究.重庆医科大学硕士学位论文. 2009
49王玉田,李长吾,李艳春.基于小波神经网络的农药荧光光谱[J]. 2008, 29(1): 84-86.
50吴新根,葛家理.关于BP算法的初步研究[J].小型微型计算机系统, 1994, 15(1): 42-45.
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53叶斌,雷燕.关于BP网中隐含层层数及其节点数选取方法浅析[J].商丘职业技术学院学报. 2004. 12(6), 52-53.
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2 A. M. Kerdock, R. Mayer and D. Bass. Longest Binary pulse Compression Codes with Given Peak Sidelobe Levels. Proeeeding of the IEEE. February 1986. Vol. 74, No.2: P366
3 M. N. Cohen, J. M. Baden, and P. E. Cohen. Biphase Codes with Minimum Peak Sidelobes. IEEE international Radar Conference. 1989: P62-66
4武文,李江等.一种低截获概率雷达信号设计与仿真[A]. CACIS. 2007.
5陈雷,汪立森,潘明海.基于DDS的低截获概率雷达信号设计实现[A]. 2008通信理论与技术新发展第十三届全国青年通信学术会议论文集(下)[C], 2008
6徐健.多相编码脉冲压缩雷达信号的优化设计与仿真.河南大学硕士学位论文. 2007.
7 E. L. Key, E. N. Fowle, and R. D. Haggarty. A Method of Sidelobe Suppression in Phase Coded Pulse Compression Systems. M. I. T. Lincoin Lab. Lexington. Tech. Rept. 209. Nov. 1959
8 A. W. Rihaczek and R. M. Golden. Range Sidelobe Suppression for Barker Codes. IEEE Trans. Aerosp. Electron. Syst. Nov. 1971. Vol. AES-7: P1087~1092
9 M. H. Ackroyd, F. Ghani. Optimum Mismatched Filters for Sidelobe Suppression. IEEE AES. Vol. Aes-9, No. 2, March 1973
10 E. D. Mese and D. Giuli. Optimal Recursive Processing in Phase-Coded Waveform Radars, IEEE Transaction on Aerospace and Electronic Systems, March 1977, vol. AES-13, PP: 163-171.
11 S. Zoraster. Minimum Peak Range Sidelobe Filters for Binary Phase-Coded Waveforms. IEEE. AES. Vol. AES-16, No. l. January 1980
12 J. M. Baden and M. N. Cohen. Optimal Peak Sidelobe Filters for Biphase Pulse Compression. IEEE International Radar Conference. 1990: P249-252
13 S. C. Park and J. F. Doherty. A Minimax Optimization Approach to Sidelobe Suppression Filter Design. IEEE Interational Radar Conference. 1996: P3113-3116.
14 F. F. Kretschmer, L. R.Welch. Sidelobe Reduction Techniques for Polyphase Pulse Compression Codes. IEEE International Radar Conference 2000
15 M. Luszczyk, D. Mucha. Kaiser-Bessel Window Weighting Function for Polyphase Pulse Compression Code. Microwaves, Radar and Wireless Communications, 2008. MIKON 2008. 17th internation Conference on. P1-4
16王丽萍,苏涛.一种迭代加权最小二乘旁瓣抑制滤波器设计[J].火控雷达技术, 2008, 37(1): 92-96.
17 H. K. Kwan and C. K. Lee. A Neural Network Approach to Pulse Radar Detection. IEEE Transaction on Aerospace and Electronic Systems, January 1993, vol. AES-29, PP9-21.
18 H. K. Kwan and C. K. Lee. Pulse Radar Detection Using a Multi-layer Neural Network. IEEE International Joint Conference on Neural Network, 1989, vol. 2, PP75-80.
19 H. K. Kwan and P. C. Tsang. Systolic Implementation of Counterpropagation Networks. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1990, PP953-956.
20 H. K. Kwan. Systolic Architectures for Hopfield network, Bam and Multi-Layer Feed-Forward Network. ISCAS, 1989, PP790-793.
21 K.Deergha Rao and G. Sridhar. Improving Performance in Pulse Radar Detection Using Neural Networks. IEEE Transaction on Aerospace and Electronic Systems, July 1995, vol. AES-31, PP1193-1198.
22 A. J. Zejak, J. A. Zatkalik and M. L. Dukic. Doppler Optimized Compression Fiters. IEEE ISSSTA’94, PP. 534-538, July 1994.
23 I. W. Guest. Application of Neural Nets to Pulse Compression in the Presence of Doppler. Communications and Signal Proeessing 1993 Proceeding of the 1993 IEEE South Afriean Symposium., PP213-217.
24刘源,许荣庆,邓维波,位寅生.基于神经网络的随机跳频信号旁瓣抑制算法.系统工程与电子技术. 2004, 26(8):1124~1128
25杨斌,武剑辉,向敬成.非线性调频信号时域副瓣抑制滤波器设计.信号处理. 1999, (15): 31~351
26位寅生,刘永坦,随机断续高频雷达波形设计和处理.电子学报. 2002, 30(3): 437~440
27李国平.雷达脉冲压缩中相位编码信号旁瓣抑制的研究.大连理工大学硕士学位论文. 2000.
28 Taylor J W, Blinchikoff H J. Quadriphase Code-a Radar Pulse Compression Signal with Unique Characteristics [J]. IEEE Trans On AES, 1988, 24(3): 156-170.
29黄仁业.脉冲压缩信号数字处理技术的研究与实现.西南工学院学报.1999,14(1):24~28
30 Chi-chang Wang, Hsuen-Chyun Shyu. An Extended Frank Code and New Technique for Implementing P3 and P4 Codes. IEEE Trans.on Aerospace and Electronic Systems. 1989, 25(4): 442~448
31 M. Burgos-Garcia, G. Ferreiro-Collar, A. A. Lopez. Digital Monopulse Receivers for Phase Modulated Signals. IEEE International Radar Conference. 2003: 640~645
32 Cz. J. Lesnik, T. W. Brenner, W. J. Kocanda. Efficient Matched Filtering of Signal with Polyphase Frank Coded Sequences. IEEE International Conference on Microwaves and Radar. 1998: 815-819, vol. 3
33高梅国,田黎育.长二相码准连续波雷达信号脉压及多目标影响.系统工程与电子技术. 2004年2月,第26卷第2期: 163-166
34张艳艳.雷达相位编码信号的研究和应用.西安:西安电子科技大学硕士论文. 2007.
35蔡凤丽.相位编码信号脉冲压缩旁瓣抑制技术研究.南京理工大学硕士学位论文. 2009.
36 K. R. Griep, J. A. Ritcey, J. J. Burlingame. Poly-Phase Codes and Optimal Filters for Multiple User Ranging. IEEE Transactions on ASE. Vol. 31, No. 2. April 1995.
37 Qinghua Zhang, el al. Wavelet Networks [J]. IEEE Trans. on Neural Networks. 1992, 3(6):889~898.
38 Pati Y C, Krishnaprasad P. S. Analysis and Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations [J]. IEEE Trans.On Neural Networks.1993, 4(1):73～85
39 Bakshi B R and Stephanopoulos G. Wavelets as Basis Functions for Localized Learning in A Multi-resolution Hierarchy [J]. IJCNN.1992, 140-145 vol.2
40 Zhang Jun, Gilbert G.Walter, et al. Wavelet Neural Networks for Function Learning [J]. IEEE Trans.on Signal Processing. 1995, 43(6): 1485～1497
41刘景艳,郭顺京,李玉东.基于模糊小波神经网络的提升机恒减速制动系统的研究[J].工矿自动化, 2010, (06) :40-43
42王艳春,李静辉,夏颖.基于小波神经网络的掌纹识别[J].齐齐哈尔大学学报(自然科学版), 2010, (03) :5-8
43吴巧媚,刘载文,王小艺,崔莉凤,连晓峰,许继平.小波神经网络在北京河湖水华预测中的应用[J].计算机工程与应用, 2010, (12) :233-235
44朱四超.基于小波神经网络的工程形变量预测模型[J].水利与建筑工程学报, 2010, (02) :103-105
45郭旭航.小波神经网络在电力谐波检测中的应用[J].科学技术与工程, 2010, (11) :2784-2788
46宋清昆,王建双,王慕坤.基于遗传算法的小波神经网络控制器设计[J].电机与控制学报, 2010, (04) :102-106
47宋弘.小波神经网络在语音识别系统中的应用[J].微电子学与计算机, 2010, (04) :122-124,128
48邓宇.基于小波神经网络的重庆市某主城区饮用水源水水质参数预测模型研究.重庆医科大学硕士学位论文. 2009
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