【作者】 贾可新；

【导师】 何子述；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2011， 博士

【摘要】 随着通信技术的不断发展,日益密集的通信环境使得宽带通信侦察接收机捕获的宽频带数据中可能包含着许多具有不同特点的信号,如固定载频的常规通信信号、跳频信号、猝发信号、扫频信号、扩频信号、各种各样的人为和非人为的干扰信号等。如此多的信号交织在一起,使得感兴趣通信信号的监测难度越来越大,因此,研究如何对复杂通信环境中的各种通信信号进行分选,剔除干扰和噪声影响,发现感兴趣通信信号,并估计相应的特征参数,减轻通信侦察系统后续处理的负担,不但是一项很富有挑战性的课题,也已成为当前通信侦察领域紧迫而艰巨的任务之一。本文主要围绕载波频率固定的常规通信信号和跳频通信信号的分选问题进行研究,主要研究成果包括:首先,在推导多相DFT滤波器组的高效实现结构的基础上,讨论了原型低通滤波器对滤波器组输出噪声项的相关性影响。在噪声方差已知时,分别推导了考虑相关性的实际情况和不考虑相关性的理想情况下的虚警概率。当噪声方差未知时,提出采用单元平均(CA)、统计排序(OS)和前向连续均值删减(FCME-CA)算法进行频域检测,并推导了上述两种情况下的CA检测器的虚警概率。仿真实验表明两种不同情况下的检测性能非常接近。在实际应用中,可考虑忽略相关性对检测性能的影响。另外,上述三种检测算法的检测性能也通过仿真实验进行了比较。针对常规通信信号分选问题,提出了基于单天线接收或阵列接收的分选方法。这两种分选方法都主要由窄带信号检测、特征参数估计和窄带信号跟踪组成。在窄带信号检测中,为了降低原始双门限检测(LAD)算法的计算量,提出了一种改进的LAD算法;为了提高检测性能,将改进LAD算法与形态学处理相结合,提出了一种增强的LAD算法。在特征参数估计中,首先讨论了一种基于单天线接收的特征参数估计方法,然后考虑阵列接收情况,引入聚类分析的思想,提出了一种基于测量集分割的特征参数估计方法。在窄带信号跟踪中,首先分别讨论了基于单天线接收和阵列接收的窄带信号判决方法,然后给出了一种窄带信号库刷新方法。根据使用的时、频、空域信息的不同,分别提出了基于空频域信息、时频域信息和时频空域信息的跳频信号分选方法。基于空频域信息的信号分选与前述基于阵列接收的常规通信信号分选的流程非常相似,不同之处仅在于窄带信号跟踪方面。在此方法中研究了一种基于密度估计聚类的测量集分割算法和一种跳频信号跟踪方法。在基于时频域信息的信号分选中,首先提出一种基于形态学处理的时频域检测与干扰剔除算法,然后讨论了跳频信号的基本特征参数的估计方法。最后提出将基本特征参数集稀释后,采用累积差值直方图(CDIF)算法和中心时刻(CT)变换算法对每一子集中的中心时刻序列去交错,获得跳频周期的估计值。为了处理频率碰撞问题,引入空域信息,提出了基于时频空域信息的信号分选方法。这种方法与上一种分选方法仅在干扰剔除和特征参数估计中略有差异,它需首先对时频域中各连通区域对应的测量集进行分割,然后才能进行特征参数估计。在此研究了一种基于形态学分水岭聚类的测量集分割算法。因解决频率碰撞问题和基本特征参数集稀释问题,都可以看作是处理测量集分割问题,故提出了基于高斯混合模型的测量集分割算法。首先简单介绍了高斯混合模型和标准的期望均值最大化(EM)算法的概念,然后在基于正态性检验的EM算法基础之上,提出了一种竞争结束EM(CSEM)算法。该算法事先并不需要已知类别个数,对模型初始参数设置的稳健性,适合于分布形状为紧凑超椭球型的特征参数集。最后通过仿真实验验证了CSEM算法的有效性,并讨论了它在测量集分割中的应用。为了提高算法对特征参数形状和例外样本稳健性,提出了两种基于支持矢量聚类(SVC)的测量集分割算法。第一种算法是基于核参数估计的多球支持矢量聚类算法(KPMSVC),该算法是在分析扩展因子和惩罚因子对聚类结果的影响基础之上提出的。由于聚类标识在大样本情况下具有很高的运算量,故KPMSVC算法适合于小样本情况下的聚类分析。为了在大样本情况下应用SVC算法,提出了一种基于网格划分的多球支持矢量聚类算法。仿真实验验证了上述两种算法的有效性,并将它们分别应用于跳频特征参数集的稀释和解决频率碰撞问题。更多还原

【Abstract】 With the continuous development of the communication technology, increasingly intensive communication environment makes the wideband data intercepted by wideband communication reconnaissance receiver possibly include various kinds of signals with different characteristics, such as fixed-frequency narrowband communication signal, frequency hopping signal, burst signal, sweeping frequency signal, spread spectrum signal and many kinds of artificial and non-artificial interference signals. So many interwined signals make it increasingly difficult to monitor these interested communication signals. Therefore, researching on how to sort all kinds of communication signals in complex communication environment, eliminate interference and noise, find interested communication signals, estimate the associated characteristic parameters, and reduce the further treatment burden of the communication reconnaissance system, not only is a very challenging issue, but also has become one of the urgent and difficult tasks for current communication reconnaissance area.This dissertation is focused on the problem of sorting fixed-frequency conventional communication signal (fixed frequency signal) and frequency hopping signal. The main achievements of this dissertation are given as follows:Firstly, the hardware-efficient architecture of polyphase DFT filter bank is derivated and when only white Gaussian noise is poured into the filter bank, the effect of the designed prototype low-pass filter on the correlation of the filter bank output is discussed in detail. When noise variance is known, the false alarm probabilities are obtained for two different conditions (practical condition and ideal condition), respectively. When noise variance unknown, some classical algorithms, such as cell-averaging(CA), order statistics(OS) and foreword consecutive mean excision(FCME) algorithm, are firstly proposed to performing detection in frequency domain, and then the false alarm probabilities of the CA detector are discussed under the above two different conditions. Simulated results show that the detection performance under the above two different conditions are very close to each other, which means that the correlation of the filter bank output can be ignored in practical application. Moreover, the detection performance of the above three detectors are compared by simulation.For the problem of sorting fixed frequency signal, two sorting methods are proposed, which are based on single antenna and array antennas, respectively. The proposed methods include three main parts: narrowband signal detection, characteristic parameter estimation, and narrowband signal tracking. In the aspect of narrowband signal detection, to reduce the computation complexity of the original localization algorithm based on double thresholding (LAD), a modified LAD algorithm is proposed. To impove the detection performance, combining the above modified LAD algorithm and morphological processing together, an enhanced LAD algorithm is proposed. For characteristic parameter estimation, a single antenna based estimation method is firstly discussed. When array antennas are used, the idea of clustering analysis is introduced and a new measurement set segmentation based estimation method is proposed. When it comes to narrowband signal tracking, two different narrowband signal decision methods are discussed and a narrowband signal library update method is studied.According to the time-domain, frequency-domain and spatial-domain information used in sorting methods, three frequency hopping signal sorting methods are proposed, which are based on frequency-spatial-domain(FSD), time-frequency-domain(TFD) and time-frequency-spatial-domain(TFSD) information, respectively. The FSD based signal sorting method is very similar to the above fixed frequency signal sorting method when array antennas are used. The difference between the above two methods is only in case of narrowband signal tracking. In this method, a density estimation clustering based measurement set segmentation algorithm and a frequency hopping signal tracking method are studied. For the TFD based signal sorting method, a morphological processing based time-frequency detection and interference eliminate algorihm is firstly proposed, and then a characteristic parameter estimation method is discussed. Finally, a frequency hopping period estimation method is investigated in detail, which dilutes the characteristic parameter set, and uses cumulative difference histogram (CDIF) and center time (CT) transform algorithm to deinterleave these CT sequences included in each diluted subset and estimate frequency hopping period. To solve frequency collision problem, the spatial information is introduced and a TFSD based sorting method is developed. Compared with TFD based method, the TFSD based sorting method only has small difference in the aspect of interference eliminate and characteristic parameter estimation. The last method need to segment the corresponding measurement set of each connected region in TFD, and then estimate characteristic parameters. In this method, a morphological watershed based clustering algorithm is studied.The problems of solving frequency collision and characteristic parameter set dilution can be regarded as processing measurement set segmentation problem. Hence, a Gaussian mixture model based measurement set segmentation algorithm is proposed. Firstly, the conception of Gaussian mixture model and standardexpectation-maximization (EM) algorithm is simply introduced. A new competitive stop EM (CSEM) algorithm is proposed, which is on the basis of normality test based EM algorithm. The proposed algorithm does not know the model order in prior, is robust to the initialized parameters of model, and suitable for compact and hyperellipsoidal sets. At last, simulation results vertify the validity of the proposed CSEM algorithm; its application in measurement set segmentation is also discussed by simulation. To improve the robustness to outliers and the shape of characteristic parameter set, two new support vector clustering(SVC) based measurement set segmentation algorithm are developed. The first algorithm is a kernel parameter estimation based multi-sphere support vector clustering (KPMSVC) algorithm, which is proposed after carefully analyzing the effect of spread factor and penality factor on clustering results. Because of the computation complexity of cluster assignment for large sample set, the proposed KPMSVC algorithm is only suitable for small sample set. In order to apply SVC for large sample set, a statistical histogram based multi-sphere support vector clustering (SHMSVC) algorithm is proposed. Simulation results vertify the validity of the above two algorithms. The above two algorithms are also applied to solve frequency collision and characteristic parameter set dilution problems.更多还原