基于AIC信息的信号分选识別技术研究
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摘要
随着无线电技术的不断发展，电磁环境日益趋于复杂化。在通信过程中通信双方很难做到完全的抗干扰、抗侦听，再加上频谱资源的有限性，使得现代通信往高频段方向发展，出现了如宽频段高速跳频、猝发、跳时等通信方式，它们有效地克服了干扰严重、抗侦听差、多径衰弱等问题，所以这些通信方式很快被运用到了军用和民用通信领域。但是在处理如超宽带跳频这样的信号时，奈奎斯特定理指出要想不失真的恢复原始信号，采样率需达到信号带宽的两倍以上，这将需要非常高的采样率和极快的数据处理速度，给现有硬件设施带来了巨大的压力，极大的制约了超宽带信号的发展。所以如何突破奈奎斯特采样定理的限制，以较低的采样率对信号进行采样成为急需解决的问题。
近年来，国外学者提出了一种名为压缩采样（Compressive Sampling，CS）的全新理论。当信号通过模拟信息转换器（Analog-to-Digital Converter,AIC）时，可以以远低于奈奎斯特率对信号进行采样，获得少量的信息样点，由此前端ADC快速采样和存储设备面对海量数据的压力能够得到有效的缓解。但通过AIC后得到的是信息域数据而不再是传统的时域数据，所以原先的信号处理方法不再可行，需寻求新的快速处理算法。本论文通过与压缩采样原理相结合，对基于AIC信息的信号分选与识别技术做了深入研究，提出了几种能应用于超宽带高速跳频通信的信息域数据处理算法。
本论文内容包括四部分：首先，从离散信号角度出发，详细地介绍了压缩采样的基本原理和实现方法，并对其中信号的稀疏分解、测量矩阵的设计和重建算法做了较深入研究。第二部分，从离散信号过渡到连续模拟信号，构建了三种模拟信息转换器——并联型AIC、分段型AIC和随机调制型AIC，并进行了数值仿真，定量分析了三种结构在不同参数下对信号重建所需输入信噪比及最小测量信息量（信息样点数）的影响。第三部分，对基于AIC信息的信号重建模型进行了分析，介绍了几种常用的信号重建算法，然后提出了一种摆脱稀疏度依赖的自适应压缩采样匹配追踪算法。通过计算机仿真将几种重建算法做了性能对比，论文提出的算法在计算复杂度和精确度方面均有所改善。第四部分，在宽频带内存在若干定频干扰信号的情况下，提出了最小均方误差算法，实现了定频干扰抑制这一目的，同时与传统的方法做了对比。根据不同的跳频频率集可以生成不同的恢复字典，将AIC信息在恢复字典上求解稀疏度最小的稀疏矢量以实现不同跳频网台分选。在跳频信号频率估计方面，通过数值仿真得出夹角法很好的完成了每一跳信号的频率估计，在不同信噪比下归一化的均方误差均另人满意，验证了这一算法的可行性。
With the rapid development of electronic technology, electromagnetic environment becomesmore complex. Modern communication developed to the direction of high frequency because theexisting communication systems are difficult to achieve the goal of anti-jamming or anti-listeningcompletely in the communication process for communicating parties and coupled with the limitedspectrum resource. So some new communication systems emerged, such as high-speed widebandfrequency hopping, burst signal, etc. They were applied to the field of military and civilian quickly,because they can overcome the drawback of serious interference, poor anti-listening and multi-pathfading. But the Nyquist theorem points out that the sampling rate should be more than twice of thesignal bandwidth in order to recover the original signal without distortion. Therefore, it needs highsampling rate and fast data processing speed when process high-speed wideband frequency hoppingsignal. It brings huge pressure to the existing hardware and restricts the development ofultra-wideband signals. So how to break the limitation of Nyquist theory and seek for lowersampling rate have become an urgent problem.
In recent years, foreign scholars have proposed a new theorem named compressivesampling(CS). This theory indicates that the sampling rate is well less than Nyquist rate when signalpasses the analog to information converter(AIC). So it can solve the problem of front ADC needshigh speed sampling rate and huge pressure on storage device. After the sampling of AIC, itbecomes information domain data, not the traditional time domain data. So conventional signalprocessing method is no longer feasible, it is necessary to search for new rapid processingalgorithms. This paper have done some research on signal sorting and recognition based on AICinformation through combining the compressive sampling theorem and proposed severalinformation domain data processing algorithms which can be used in communication systems justlike UWB signal, frequency hopping.
There are four parts in this paper. PartⅠ:From the perspective of discrete signals, this papermakes a full introduction about basic principles and implementation of compressive sampling. Thendo some in-depth study on spare decomposition of the signal, design of the measurement matrix,reconstruction algorithms. PartⅡ:The article construction three AIC structures: parallel type,segment type and pre-modulation type transition from discrete signals to continuous analog signals.It dose some simulations and analyzes the impact of input signal to noise ration which signalreconstruction needed and minimum measurement points under different AIC structures andparameters. PartⅢ:It describes several signal reconstruction algorithms based on signal reconstruction model of AIC information. This paper presents an adaptive compressive samplingmatching pursuit(ACSMP) algorithm. The algorithm can get rid of the dependence on sparsity, andcan approach the original signal through adjust the step size adaptively in iteration. The simulationresults show that the proposed algorithm can reconstruct the signal accurately, the probability ofreconstruction and the computational complexity are better than others. PartⅣ: It proposes minimum meansquare error algorithm to achieve the goal of fixed frequency suppression and compares withconventional algorithms. Because of different frequency of frequency-hopping signals can productdifferent reconstruction dictionary, this paper presents an algorithm to achieve the aim of sortingfrequency-hopping signals through solve the minimum sparsity of sparse vector by the informationof AIC. In the filed of frequency estimation about frequency hopping signal, the simulation resultsof subspace angle algorithm display that this algorithm can well estimate the frequency offrequency hopping signal under different signal to noise ration and the feasibility of this algorithm.
近年来，国外学者提出了一种名为压缩采样（Compressive Sampling，CS）的全新理论。当信号通过模拟信息转换器（Analog-to-Digital Converter,AIC）时，可以以远低于奈奎斯特率对信号进行采样，获得少量的信息样点，由此前端ADC快速采样和存储设备面对海量数据的压力能够得到有效的缓解。但通过AIC后得到的是信息域数据而不再是传统的时域数据，所以原先的信号处理方法不再可行，需寻求新的快速处理算法。本论文通过与压缩采样原理相结合，对基于AIC信息的信号分选与识别技术做了深入研究，提出了几种能应用于超宽带高速跳频通信的信息域数据处理算法。
本论文内容包括四部分：首先，从离散信号角度出发，详细地介绍了压缩采样的基本原理和实现方法，并对其中信号的稀疏分解、测量矩阵的设计和重建算法做了较深入研究。第二部分，从离散信号过渡到连续模拟信号，构建了三种模拟信息转换器——并联型AIC、分段型AIC和随机调制型AIC，并进行了数值仿真，定量分析了三种结构在不同参数下对信号重建所需输入信噪比及最小测量信息量（信息样点数）的影响。第三部分，对基于AIC信息的信号重建模型进行了分析，介绍了几种常用的信号重建算法，然后提出了一种摆脱稀疏度依赖的自适应压缩采样匹配追踪算法。通过计算机仿真将几种重建算法做了性能对比，论文提出的算法在计算复杂度和精确度方面均有所改善。第四部分，在宽频带内存在若干定频干扰信号的情况下，提出了最小均方误差算法，实现了定频干扰抑制这一目的，同时与传统的方法做了对比。根据不同的跳频频率集可以生成不同的恢复字典，将AIC信息在恢复字典上求解稀疏度最小的稀疏矢量以实现不同跳频网台分选。在跳频信号频率估计方面，通过数值仿真得出夹角法很好的完成了每一跳信号的频率估计，在不同信噪比下归一化的均方误差均另人满意，验证了这一算法的可行性。
With the rapid development of electronic technology, electromagnetic environment becomesmore complex. Modern communication developed to the direction of high frequency because theexisting communication systems are difficult to achieve the goal of anti-jamming or anti-listeningcompletely in the communication process for communicating parties and coupled with the limitedspectrum resource. So some new communication systems emerged, such as high-speed widebandfrequency hopping, burst signal, etc. They were applied to the field of military and civilian quickly,because they can overcome the drawback of serious interference, poor anti-listening and multi-pathfading. But the Nyquist theorem points out that the sampling rate should be more than twice of thesignal bandwidth in order to recover the original signal without distortion. Therefore, it needs highsampling rate and fast data processing speed when process high-speed wideband frequency hoppingsignal. It brings huge pressure to the existing hardware and restricts the development ofultra-wideband signals. So how to break the limitation of Nyquist theory and seek for lowersampling rate have become an urgent problem.
In recent years, foreign scholars have proposed a new theorem named compressivesampling(CS). This theory indicates that the sampling rate is well less than Nyquist rate when signalpasses the analog to information converter(AIC). So it can solve the problem of front ADC needshigh speed sampling rate and huge pressure on storage device. After the sampling of AIC, itbecomes information domain data, not the traditional time domain data. So conventional signalprocessing method is no longer feasible, it is necessary to search for new rapid processingalgorithms. This paper have done some research on signal sorting and recognition based on AICinformation through combining the compressive sampling theorem and proposed severalinformation domain data processing algorithms which can be used in communication systems justlike UWB signal, frequency hopping.
There are four parts in this paper. PartⅠ:From the perspective of discrete signals, this papermakes a full introduction about basic principles and implementation of compressive sampling. Thendo some in-depth study on spare decomposition of the signal, design of the measurement matrix,reconstruction algorithms. PartⅡ:The article construction three AIC structures: parallel type,segment type and pre-modulation type transition from discrete signals to continuous analog signals.It dose some simulations and analyzes the impact of input signal to noise ration which signalreconstruction needed and minimum measurement points under different AIC structures andparameters. PartⅢ:It describes several signal reconstruction algorithms based on signal reconstruction model of AIC information. This paper presents an adaptive compressive samplingmatching pursuit(ACSMP) algorithm. The algorithm can get rid of the dependence on sparsity, andcan approach the original signal through adjust the step size adaptively in iteration. The simulationresults show that the proposed algorithm can reconstruct the signal accurately, the probability ofreconstruction and the computational complexity are better than others. PartⅣ: It proposes minimum meansquare error algorithm to achieve the goal of fixed frequency suppression and compares withconventional algorithms. Because of different frequency of frequency-hopping signals can productdifferent reconstruction dictionary, this paper presents an algorithm to achieve the aim of sortingfrequency-hopping signals through solve the minimum sparsity of sparse vector by the informationof AIC. In the filed of frequency estimation about frequency hopping signal, the simulation resultsof subspace angle algorithm display that this algorithm can well estimate the frequency offrequency hopping signal under different signal to noise ration and the feasibility of this algorithm.
引文
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[2]金艳.低截获概率信号的循环平稳检测与参数估计研究[D].西安：西安电子科技大学博士学位论文，2008.
[3]倪敢峰.低截获概率雷达技术研究[D].南京：南京理工大学硕士论文，2007.
[4]许梅.超宽带（UWB）通信抗截获技术研究[D].西安：西安电子科技大学硕士学位论文，2009.
[5]程安启，王华力.基于卫星平面阵列多波束天线波束空间的自适应调零和干扰源定位技术[J].电子学报，2008,36（7）：1279-1290.
[6]赵俊，张朝阳，赖利峰，曹千芊.一种基于时频分析的跳频信号参数盲估计方法[J].电路与系统学报，2003,8（3）：46-50.
[7]范海宁，郭英.一种新的跳频信号参数盲估计算法[J].信号处理，2009，25（11）：1754-1758.
[8]冯涛.跳频信号的时频分析新方法[J].北京邮电大学学报，2010,33（3）：10-14.
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