数字通信信号调制识别算法研究
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摘要
空间传播的通信信号采用了不同的调制方式。在许多应用中，需要监视通信信号的活动情况，区分信号的性质，甚至截获其传输的信息内容。例如，政府有关职能部门要监视民用通信信号，以实现干扰识别和电磁频谱管理。特别是在军事应用中，通信情报系统作为通信电子战(或信息战)的电子支援措施之一，用宋监视战场的电磁频谱活动，进行威胁识别，帮助选择电子干扰策略，直至截获敌方的有用军事情报。
调制方式是区分不同性质通信信号的一个重要特征，而要截获通信信号的信息内容，必须知道信号的调制方式和调制参数。给定一段接收的通信信号，调制识别的目的就是在未知调制信息内容的前提下，判断出通信信号的调制方式，并估计出相应的调制参数。
本文在前人工作的基础上，通过对信号累量域不变量特征的分析，深入研究了MPSK、MASK和MQAM数字通信信号的调制识别问题，主要工作可概括如下：
1．从通信信号截获的工程实用角度出发，提出了“大信噪比”条件下的次优似然比分类算法。新方法结合了平均似然比分类性能好和标准的广义似然比分类计算量小的优点。同时解决了信号码元集合存在包含关系时，标准的广义似然比分类算法的失效问题。通过理论分析“大信噪比”近似成立的条件，把调制分类性能与信号解调的误码串联系在一起。据我们所知，本文是第一次定量地分析调制识别所需的信号环境与正常解调所需的信号环境之间的关系。对新分类方法的渐进性能分析和计算机仿真表明，在满足“大信噪比”条件下，当观测数据足够长时，正确分类概率趋于100％。
2．针对MPSK信号似然比分类中的参数估计问题，提出了在未知调制类型的前提下，基于累量分析的(对调制类型)盲的信噪比和参考相位估计算法，并理论分析了累量函数估计值的渐进统计分布特性。把基于累量分析的信号参数估计算法用于已知调制类型的MASK、MQAM信号，通过大量的计算机仿真，初步考察了算法的性能。
3．提出了在高斯噪声和理想信道环境下，基于高阶累量不变量特征的MPSK、MASK和MQAM信号调制分类算法，新特征对信噪比和未知的参考相位参数是盲的。针对MPSK信号分类问题，讨论了分类算法的理论渐进性能，并通过大量的计算机仿真实验证实了分类算法的有效性。最后针对不同调制子类信号的特点，我们分别给出了递归降阶调制识别方法，从而在满足一定信噪比条件下，当观测数据长度足够长时，本文算法理论上可分类任意调制阶数的数字通信信号。
4．把高斯理想信道条件下基于累量不变量特征的调制分类算法，推广到高斯多径信道环境中。在已知多径参数的条件下，我们提出了基于多径累量不变量特征的信号分类算法。与盲均衡加理想信道累量不变量分类算法相比，多径累量不变量分类方法仅需要知道多径信道FIR模型的阶数。当信道模型的阶数也未知时，我们提出了把
11 数字通信信号调制识别算法研究
高斯理想信道中的累量不变量特征用于多径信道时，基于累量近似不变量分类特征的
调制识别算法。理论分析和计算机仿真实验表明，本文方法在未知信道参数时，具有
很好的工程实用性。
5．研究了非高斯噪声、多信号和多径信道环境下，MPSK信号的调制识别问题。
利用复基带通信信号的循环平稳特性，提出了基于循环累量不变量分类特征的＼IPSK
信号分类算法，从而把基于平稳时间序列模型的累量不变量分类方法推广到循环平稳
域。
6．研究了hSK、MQAM和MPSK信号调制子类间的分类识别问题。考察2；。’4／SPSK
信号与 MASK、MQAM和 MPSK信号调制子类间的关系表明，可利用 2／4／SPSK信号分类
算法实现不同调制子类间的识别。对更高调制阶数MPSK、MQAM和AISK信号分类的计
算机仿真试验，证实了子类分类算法的有效性。同时还初步讨论了采用判决树和神经
网络分类器时，本文提出的不变量分类特征的分类性能。
Communication signals travel in space with different modulation formats. In many applications, it is required to monitor the activities of these signals, identify their characteristics, even to intercept the signal information content. For instance, Civilian authorities may wish to monitor their transmissions in order to implement signal confirmation, interference identification and spectrum management. Especially in military applications, communication intelligence system, as one of the Electronic Support Measures (ESM) in Electronic Warfare (EW), is used to monitor electromagnetic spectra activities, implement threat detection and warning, and help to select jamming strategy and to intercept useful military intelligence.
Modulation format is one of the most important characteristics used to distinguish communication signals. Knowing the modulation format and modulation parameters of a communication signal is the first step of correct demodulation. Given a received communication signal, the objective of modulation recognition is to decide the modulation format and estimate the modulation parameters of the communication signal without any priori knowledge about the signal information content.
Based on the analysis of invariant features in cumulant domain of communication signals, the classification of communication signals with MPSK, MASK and MQAM modulation formats is investigated this dissertation. The main works can be summarized as follows:
1. A new sub-optimum likelihood ratio classification algorithm is proposed at the assumption that the communication signals be received in "Higher Signal to Noise Ratio (HSNR)" environment with engineering practice significance. The new algorithm integrates the benefit of the average likelihood ratio algorithm with higher correct classification probability and that of the standard general likelihood ratio algorithm with low computation complexity, meanwhile, overcomes the invalidation problem of standard general likelihood ratio algorithm, when there are including relations between constellation sets of communication signals to be recognized. Through theoretically analyzing the HSNR condition for our algorithm to be applied, we relate the correct classification probability and bit error ratio of demodulation. To the best of our knowledge, this is the first time that modulation classification algorithm considers quantificationally the relations between signal environment of modulation classification and that of signal demodulation. The theoretical analysis and computer simulations reveal that, with HSNR conditions
satisfied and enough data received, the correct classification probability of our sub-optimum algorithm can achieve about 100%.
2. A new cumulant based algorithm of estimating Signal to Noise Ratio (SNR) and reference phase is proposed for likelihood ratio classification of MPSK signals, which is blind to unknown phase order of MPSK signals. The asymptotic statistic distribution characteristics of cumulant function estimation of MPSK signals are given. The performance of our parameter estimation algorithm applied to MASK and MQAM signals with known level order are preliminarily investigated through computer simulation.
3. The new algorithms for classification of MPSK, MASK and MQAM signals using cumulant invariants are proposed in Gaussian noise and ideal communication channel environment. The new classification features are blind to unknown SNR and reference phase. The asymptotic performances of our algorithms are verified through theoretical analysis and/or computer simulations. According to the respective properties of different signal sub-set, we give recursive order-reducing classification algorithm. So that, beyond certain SNR and with enough received data, theoretically, our classification algorithms can recognize digital communication signals with any modulation order.
4. Extending cumulant invariants based signal classification algorithms to Gaussian multipath channel environment, we proposed recognition algorithm
调制方式是区分不同性质通信信号的一个重要特征，而要截获通信信号的信息内容，必须知道信号的调制方式和调制参数。给定一段接收的通信信号，调制识别的目的就是在未知调制信息内容的前提下，判断出通信信号的调制方式，并估计出相应的调制参数。
本文在前人工作的基础上，通过对信号累量域不变量特征的分析，深入研究了MPSK、MASK和MQAM数字通信信号的调制识别问题，主要工作可概括如下：
1．从通信信号截获的工程实用角度出发，提出了“大信噪比”条件下的次优似然比分类算法。新方法结合了平均似然比分类性能好和标准的广义似然比分类计算量小的优点。同时解决了信号码元集合存在包含关系时，标准的广义似然比分类算法的失效问题。通过理论分析“大信噪比”近似成立的条件，把调制分类性能与信号解调的误码串联系在一起。据我们所知，本文是第一次定量地分析调制识别所需的信号环境与正常解调所需的信号环境之间的关系。对新分类方法的渐进性能分析和计算机仿真表明，在满足“大信噪比”条件下，当观测数据足够长时，正确分类概率趋于100％。
2．针对MPSK信号似然比分类中的参数估计问题，提出了在未知调制类型的前提下，基于累量分析的(对调制类型)盲的信噪比和参考相位估计算法，并理论分析了累量函数估计值的渐进统计分布特性。把基于累量分析的信号参数估计算法用于已知调制类型的MASK、MQAM信号，通过大量的计算机仿真，初步考察了算法的性能。
3．提出了在高斯噪声和理想信道环境下，基于高阶累量不变量特征的MPSK、MASK和MQAM信号调制分类算法，新特征对信噪比和未知的参考相位参数是盲的。针对MPSK信号分类问题，讨论了分类算法的理论渐进性能，并通过大量的计算机仿真实验证实了分类算法的有效性。最后针对不同调制子类信号的特点，我们分别给出了递归降阶调制识别方法，从而在满足一定信噪比条件下，当观测数据长度足够长时，本文算法理论上可分类任意调制阶数的数字通信信号。
4．把高斯理想信道条件下基于累量不变量特征的调制分类算法，推广到高斯多径信道环境中。在已知多径参数的条件下，我们提出了基于多径累量不变量特征的信号分类算法。与盲均衡加理想信道累量不变量分类算法相比，多径累量不变量分类方法仅需要知道多径信道FIR模型的阶数。当信道模型的阶数也未知时，我们提出了把
11 数字通信信号调制识别算法研究
高斯理想信道中的累量不变量特征用于多径信道时，基于累量近似不变量分类特征的
调制识别算法。理论分析和计算机仿真实验表明，本文方法在未知信道参数时，具有
很好的工程实用性。
5．研究了非高斯噪声、多信号和多径信道环境下，MPSK信号的调制识别问题。
利用复基带通信信号的循环平稳特性，提出了基于循环累量不变量分类特征的＼IPSK
信号分类算法，从而把基于平稳时间序列模型的累量不变量分类方法推广到循环平稳
域。
6．研究了hSK、MQAM和MPSK信号调制子类间的分类识别问题。考察2；。’4／SPSK
信号与 MASK、MQAM和 MPSK信号调制子类间的关系表明，可利用 2／4／SPSK信号分类
算法实现不同调制子类间的识别。对更高调制阶数MPSK、MQAM和AISK信号分类的计
算机仿真试验，证实了子类分类算法的有效性。同时还初步讨论了采用判决树和神经
网络分类器时，本文提出的不变量分类特征的分类性能。
Communication signals travel in space with different modulation formats. In many applications, it is required to monitor the activities of these signals, identify their characteristics, even to intercept the signal information content. For instance, Civilian authorities may wish to monitor their transmissions in order to implement signal confirmation, interference identification and spectrum management. Especially in military applications, communication intelligence system, as one of the Electronic Support Measures (ESM) in Electronic Warfare (EW), is used to monitor electromagnetic spectra activities, implement threat detection and warning, and help to select jamming strategy and to intercept useful military intelligence.
Modulation format is one of the most important characteristics used to distinguish communication signals. Knowing the modulation format and modulation parameters of a communication signal is the first step of correct demodulation. Given a received communication signal, the objective of modulation recognition is to decide the modulation format and estimate the modulation parameters of the communication signal without any priori knowledge about the signal information content.
Based on the analysis of invariant features in cumulant domain of communication signals, the classification of communication signals with MPSK, MASK and MQAM modulation formats is investigated this dissertation. The main works can be summarized as follows:
1. A new sub-optimum likelihood ratio classification algorithm is proposed at the assumption that the communication signals be received in "Higher Signal to Noise Ratio (HSNR)" environment with engineering practice significance. The new algorithm integrates the benefit of the average likelihood ratio algorithm with higher correct classification probability and that of the standard general likelihood ratio algorithm with low computation complexity, meanwhile, overcomes the invalidation problem of standard general likelihood ratio algorithm, when there are including relations between constellation sets of communication signals to be recognized. Through theoretically analyzing the HSNR condition for our algorithm to be applied, we relate the correct classification probability and bit error ratio of demodulation. To the best of our knowledge, this is the first time that modulation classification algorithm considers quantificationally the relations between signal environment of modulation classification and that of signal demodulation. The theoretical analysis and computer simulations reveal that, with HSNR conditions
satisfied and enough data received, the correct classification probability of our sub-optimum algorithm can achieve about 100%.
2. A new cumulant based algorithm of estimating Signal to Noise Ratio (SNR) and reference phase is proposed for likelihood ratio classification of MPSK signals, which is blind to unknown phase order of MPSK signals. The asymptotic statistic distribution characteristics of cumulant function estimation of MPSK signals are given. The performance of our parameter estimation algorithm applied to MASK and MQAM signals with known level order are preliminarily investigated through computer simulation.
3. The new algorithms for classification of MPSK, MASK and MQAM signals using cumulant invariants are proposed in Gaussian noise and ideal communication channel environment. The new classification features are blind to unknown SNR and reference phase. The asymptotic performances of our algorithms are verified through theoretical analysis and/or computer simulations. According to the respective properties of different signal sub-set, we give recursive order-reducing classification algorithm. So that, beyond certain SNR and with enough received data, theoretically, our classification algorithms can recognize digital communication signals with any modulation order.
4. Extending cumulant invariants based signal classification algorithms to Gaussian multipath channel environment, we proposed recognition algorithm
引文
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