【作者】 廖红舒；

【导师】 魏平；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2011， 博士

【摘要】 通信侦察是现代战争中获取军事情报的重要手段,也是实施电子干扰与摧毁的前提。随着现代战争信息化程度越来越高,现代军事通信呈现出软件化、智能化、宽带化和网络化的发展趋势,并产生了如高速跳频、突发通信、复杂调制和复杂编码等的高新技术,这些技术在军事通信中的应用给通信侦察带来了难题,主要表现在:(1)截获信号往往存在时、频域严重混叠现象;(2)截获突发通信信号时侦察方截获数据样本较短;(3)侦察接收机常常面临非视距接收、旁瓣侦察及多径衰落信道等非理想条件,都将导致接收信号质量不高甚至失真。因此,通信侦察中的信号处理需要解决的问题不再是单一化,而是多样性和复杂性并存。基于这些问题的考虑,一个完整的适合通信侦察的信号处理流程应该具有信号分离,均衡信道效应,适应低信噪比、短数据的参数估计以及复杂信号类型识别等功能。本文的工作正是围绕以上问题开展研究,归纳起来本文的主要贡献为:1.针对信噪比估计,比较了各种信息论准则对信号子空间维数估计的性能影响,为实际应用提供了参考依据。利用随机矩阵理论,重点分析了短数据对样本协方差矩阵特征值的影响,通过利用AIC准则的极大似然函数仅与由噪声的特征值构成的函数有关的特点,提出利用改进的信号子空间维数的估计方法用于短数据的信号子空间维数的估计,从而得到稳健的信噪比估计方法。2.充分利用了双平行线阵的结构特点,采用了ESPRIT方法实现了对二维波达方向估计,既避免了孔径损失使参数估计的性能得到提高,又完成了对方位角与俯仰角的自动配对,还克服常见二维波达方向估计中存在的角度兼并问题。3.分析了基于牛顿迭代nc-FastICA方法分离载波调制非圆信号(如BPSK)的缺陷,是由于由信号峭度改变(从亚高斯趋向于超高斯)超过其稳定分离区域所导致的,并提出了一种通用的非线性函数用于分离算法,从而解决了载波调制非圆信号的稳定分离问题。4.针对基于二阶统计量的信道盲估计方法应用于DSSS信号时,由于传输延时的不确定性导致信道首尾小项效应以及阶数不固定的问题,提出了一种新的预处理方法,给出了确定最佳接收起始时刻的最优准则,并进行了详细的理论推导与证明,实现了不依赖于阶数估计就能达到解决信道首尾小项效应问题的目的。5.针对信号调制识别问题,研究了复杂调制类型信号的识别方法,以及复杂信号环境如低信噪比、多径衰落信道条件下的调制类型识别方法。对于复杂调制类型信号的识别,提出了具有自适应选择最优尺度的小波变换识别方法以及基于HHT变换时频图的识别方法,实现了高阶PSK与QAM信号及其子类的正确识别;对于低信噪比接收条件的识别问题,通过利用噪声不同时刻的不相关性,提出了基于错位相乘的频域识别方法;对于多径衰落信道下的识别问题,提出了阵列接收与双模式盲均衡相结合的处理思路。更多还原

【Abstract】 Communication reconnaissance is one of the most important methods to obtain military information and is also the basis in implementing electronic interference and damage. With the increasing information level in modern wars, lots of new technologies such as high speed frequency-hopping, burst communication, complicated modulation and code are widely used in military communication field and make military communication present tendency of software, intelligence, broadband and network. However, the application of these new techs would bring big challenges to communication reconnaissance, such as: (1) the intercepted signal could be probably multi-signal with time-frequency overlapping seriously; (2) only small size data samples can be used due to the low interception probability communication technology; (3) the received signal’s qulity would be much more worse because of non-line-of-sigh reconnaissance, sidelobe reconnaissance and multipath effects.Therefore, signal processing in communication reconnaissance is not a simple task but a problem more diversity and complex. Base on the consideration, a new complete signal process should have the capability of multi-signal separation, channel equalization, parameters estimation adapted to low SNR and small size data, and modulation classification in complicated envirement. The main contributions of this dissertation are studying the solutions to such problems, they are summarized as following:1. The non-data aided signal to noise ratio (SNR) estimation problem is first studied. After comparing various information criteria performance in estimating SNR, the suitable criterion is given in practical application. The effects when small size data is used in calculating eigenvalues of sample covariance matrix are also analized with the help of random matrix theory. By using the properties that the likelihood function of AIC criteria depends only on function of noise space eigenvalues, a new robust SNR estimation method is proposed with the application of modified-AIC criterion. 2. Based on the characteristics of two parallel uniform linear arrays (ULAs), a novel 2-D ESPRIT algorithm has been proposed. The proposed method employs the shift invariance property of the array geometry and it can estimate precisely for avoiding decreasing the array aperture. This proposed method not only has an advantage of automatically parameter alignment but also handle sources with common 1-D angles.3. The limitation of nc-FastICA method based on Newton iteration is analized, which can not separate the modulated non-circular signal, for instance, BPSK signal. The reason is that the kurtsis of modulated BPSK signal changed and exceeded the stable range of nc-FastICA method. Therefore a general non-linear function is proposed and applied to the separation algorithm, and the robust separation of modulated non-circular signal is completed.4. With the aim of solving problem that channel blind identification methods based on second-order characteristics perform badly when DSSS signals are used to be sources, a criterion is derived to determine appropriate starting point of receiving data as a preprocessing algorithm. The new method avoids channel order estimation and shows more robust in the situation when small leading and/or trailing terms are existed.5. Finally two time-frequency methods are proposed which are based on wavelet and Hilbert-huang transformings and complete the intra and inner modulation classification of higher order PSK-type and QAM-type signals. By using the uncorreration property of Gaussian noise at different moments, a new frequency domain classification algorithm is presented which can adapt to work under lower SNR scenario. An idea of joining array receiving mode and dual mode blind equalization is also given as to the modulation classification problem under multipath channel.更多还原