A Method for Simulating Complex Nakagami Fading Time Series With Nonuniform Phase and Prescribed Autocorrelation Characteristics

The availability of an accurate and systematic channel simulation technique is critical for the verification of the performance of digital radio transceivers designed for use on wireless channels. Despite the abundant results on channel simulation techniques available in the literature, an accurate technique for simulating Nakagami- $m$ fading signals that have nonuniform phase distributions and any prespecified temporal autocorrelation function is not yet available. Such a technique is reported for the first time in this paper. We develop new cumulative distribution function (cdf) mapping methods to generate complex Nakagami sequences from complex Gaussian sequences, based on the independent transformation of their real and imaginary parts. Additionally, we analyze the relationships between the autocorrelation functions of Rayleigh and Nakagami fading signals to determine the autocorrelation function of Rayleigh input that is required to produce a specified autocorrelation function for the Nakagami output. Then, we implement the mapping algorithm to transform Rayleigh sequences with the so-determined autocorrelation functions into Nakagami sequences with the desired prespecified autocorrelation functions. Simulation results verify that our approaches can lead to the accurate simulation of Nakagami fading signals with prespecified autocorrelation functions and nonuniform phase distributions.      

Robust Symbol Rate Estimation of PSK Signals Under the Cyclostationary Framework

In many communication channels the noise is non-Gaussian and usually exhibits impulsive characteristics. The symbol rate estimation problem of phase-shift keying (PSK) signals in this kind of impulsive noise environment is addressed. Since the performance of the cyclic statistics-based symbol rate estimation methods, which are very effective in Gaussian noise, may deteriorate significantly in the presence of impulsive noise, a robust method under the cyclostationary framework is proposed. Specifically, a robust form of cyclic autocorrelation based on the M-estimate concept is developed. Simulation results show that the proposed robust technique offers a significant performance gain over the cyclic autocorrelation method in impulsive noise.     

基于延迟自相关特性的码速率测量技术

通信信号的识别与解调是通信侦察的重要研究内容,在通常情况下,通信信号的很多参数并不知道。本文介绍了一种基于通信信号码延迟自相关特性的码速率测量技术,并给出了码速率测量理论分析及基于硬件的实现技术,仿真和实验结果表明本方案具有较高的测量性能。     

General results in the mathematical theory of random signals and noise in nonlinear devices

An analysis is made of the output resulting from passing signals and noise through general zero memory nonlinear devices. New expressions are derived for the output time function and autocorrelation function in terms of weighted averages of the nonlinear characteristic and its derivatives. These expressions are not restricted to Gaussian noise and apply to any nonlinearity having no more than a finite number of discontinuities. The method of analysis used is heuristic.     

非周期长码直扩信号的长扰码识别

该文利用m序列的二阶和三阶自相关特性,推导得到非周期长码直扩信号三阶自相关函数的极大似然估计,利用m序列的线性移位叠加特性和三阶自相关函数特性,提出了非合作通信情况下消除信息码对长码估计影响的延迟相乘方法和基于三阶自相关峰的长码识别算法,该识别算法只需已知信号的载波频率和扩频码码片速率。在加性高斯白噪声下的仿真表明,仅利用1/4倍长码周期长度的信号,在信噪比大于3.5 dB时长码m序列的正确识别概率就达到90%以上。     

一种任意信号源盲分离的高效算法

提出了信号源盲分离的DBBSS算法.利用随机变量概率密度函数非参数估计的核函数法,对混合信号的概率密度函数及其导数进行估计,并由此估计信号的评价函数(score function).解决了现有信号源盲分离算法中,普遍存在的非线性函数只能凭经验选取,以及混合信号同时包含超高斯信号和亚高斯信号时,算法失效的问题.该方法非常简单,可以直接应用于所有以非线性函数代替评价函数的信号源盲分离算法.仿真结果验证了算法的有效性.     

一种利用多音激励估算非线性失真的方法

针对射频通讯电路中的非线性失真,将非线性电路简化为三阶无记忆模型.利用信号的互补累积分布函数,讨论不同音数的多音信号同窄带高斯白噪声的关系.通过时域随机相位平均的原理,得到了较少音数(K<100)情况下进行非线性参数精确仿真和测试的方法,减少了由于多音音数较少而引起的仿真误差和波动.     

基于三阶累积量的语音信号的基音周期检测

本文提出了一种新的语音信号的基音周期检测方法,该方法根据语音信号的三阶累积量去确定语音信号的基音周期,能有效地排除白色或有色的高斯加性噪声所带来的干扰.与传统的基音周期估计的自相关函数法或平均幅度差函数法(AMDF)相比,该方法更精确、有效,具有更强的鲁棒性.     

Stochastic gradient based third-order Volterra system identification by using nonlinear Wiener adaptive algorithm

The nonlinear Wiener stochastic gradient adaptive algorithm for third-order Volterra system identification application with Gaussian input signals is presented. The complete self-orthogonalisation procedure is based on the delay-line structure of the nonlinear discrete Wiener model. The approach diagonalises the autocorrelation matrix of an adaptive filter input vector which dramatically reduces the eigenvalue spread and results in more rapid convergence speed. The relationship between the autocorrelation matrix and cross-correlation matrix of filter input vectors of both nonlinear Wiener and Volterra models is derived. The algorithm has a computational complexity of O(M/sup 3/) multiplications per sample input where M represents the length of memory for the system model, which is comparable to the existing algorithms. It is also worth noting that the proposed algorithm provides a general solution for the Volterra system identification application. Computer simulations are included to verify the theory.     

Calculation of Intermodulation from a Single Carrier Amplitude Characteristic

The intermodulation analysis presented in this paper is based on the important observation that any output component of a memoryless nonlinear device can be expressed as an integral of the product of two functions, one being the single carrier amplitude characteristic of the nonlinear device and the other being a function of the statistical parameters of the input signal. The analysis can also be extended to nonlinear devices with AM-PM conversion by expressing the amplitude characteristic as a complex function. The signal-dependent function is given analytically for two simple but important types of signals, for two sinusoidal carriers of equal level, and for Gaussian noise, and it is demonstrated that good agreement has been obtained when the method has been applied to microwave devices like high powerX-band klystrons and traveling wave tubes. It is also shown that the method is convenient for analysis of intermodulation in cascaded nonlinear elements for which the individual single carrier amplitude characteristics are known.     

泛Kriging法在海上短波通信频率预测中的应用

频率预测是保障短波通信的关键技术。针对现有短波频率预测方法中数据处理过程复杂、输入参数条件苛刻、工程实现难度较大等问题,提出了一种基于泛克里格(Universal Kriging)空间重构法的海上短波通信频率预测方法。利用海军频率管理系统中大量的实用通信频率数据,基于球状模型、指数模型、高斯模型三种典型变异函数理论模型,研究了通信频率的空间相关性,建立了反映沿纬度方向通信频率变化剧烈程度的漂移表达式,通过交叉重构法对海上短波通信频率进行预测。实验结果表明,泛克里格法在海上短波通信频率预测中具有良好的适用性和可靠性,对未知海域短波通信频率的预测优选具有重要意义。     

基于脉冲神经网络的雷达辐射源调制类型识别

面对日益复杂的电磁环境和层出不穷的新体制雷达,基于人工方式提取雷达辐射源特征难以满足现代认知电子战的需求.为提升雷达辐射源识别的智能化水平,提出一种新的基于脉冲神经网络(Spiking Neuron Network,SNN)进行雷达辐射源调制类型识别的算法.首先利用时频分析的方法,将5种常见雷达时域信号转换为二维灰度图...     

宽带信号下的双基地MIMO雷达收发角估计

针对双基地多输入多输出(MIMO)雷达收发角联合估计问题,利用信号的循环平稳特性,构造宽带循环平稳信号下接收数据的循环自相关矩阵。对矩阵进行特征值分解,利用MUSIC, ESPRIT等空间谱估计算法估计出信号的收发角。宽带信号能够携带更多的信息量,利于不断增加的实际需求,而信号的循环平稳特性能够很好地抗干扰以及消除高斯噪声带来的影响。实验仿真结果表明,算法在宽带循环平稳信号下具有良好的角度估计性能。     

基于循环谱分析的航空通信干扰信号检测

随着现代通信技术在航空领域的广泛应用,在机场环境下如何避免各干扰信号对航空通信的危害成为人们广泛关注的焦点问题。本文基于机场环境下的信号感知模型,提出一种基于支持向量机（SVM）和谱相关性的干扰信号感知算法,有效避免了外界信号对航空通信信号的干扰。该算法采用信号循环谱进行分析的方法提取特征向量,进而结合SVM分类器对航空干扰信号进行检测。仿真结果表明,本文算法对干扰信号具有较高的分类检测精度和良好的识别性能。     

Efficient Optimization of Reference-Based Negentropy for Noncircular Sources in Complex ICA

Bingham proposed a complex fast independent component analysis (c-FastICA) algorithm to approximate the nengentropy of circular sources using nonlinear functions. Novey proposed extending the work of Bingham using information from a pseudo-covariance matrix for noncircular sources, particularly for sub-Gaussian noncircular signals such as binary phase-shift keying signals. Based on this work, in the present paper we propose a new reference-based contrast function by introducing reference signals into the negentropy, upon which an efficient optimization FastICA algorithm is derived for noncircular sources. This new approach is similar to Novey’s nc-FastICA algorithm, but differs in that it is much more efficient in terms of the computational speed, which is significantly notable with a large number of samples. In this study, the local stability of our reference-based negentropy is analyzed and the derivation of our new algorithm is described in detail. Simulations conducted to demonstrate the performance and effectiveness of our method are also described.     

Spectral analysis of nonlinear amplifier based on the complex gainTaylor series expansion

A method of spectral analysis for a nonlinear amplifier driven by a Gaussian signal is presented. The technique is based on the Taylor series expansion of the complex gain of an amplifier. Therefore, it takes into account both amplitude-amplitude modulation (AM/AM) and amplitude-phase modulation (AM/PM) conversions and requires only an input signal autocorrelation (or power spectral density) and AM/AM and AM/PM measurements without any additional calculations     

基于椭圆球面波信号的连续相位调制及其频谱性能分析

椭圆球面波信号（PSWF）是时频域最佳能量聚集性信号,探索性地将PSWF信号应用于连续相位调制（CPM）技术,对基于椭圆球面波信号的CPM调制信号产生过程进行了描述;给出了采用自相关函数法对基于PSWF的CPM调制信号功率谱密度计算方法;结合数值计算、分析对比了最小频移键控、正弦频移键控、高斯最小频移键控以及基于高斯函数的CPM调制信号功率谱密度及其信号占用带宽等特性。数值计算结果表明椭圆球面波信号相比矩形脉冲、升余弦脉冲、高斯脉冲及（类）高斯脉冲作为CPM的基带调频信号,可以获得频谱性能、能量聚集性较好的CPM信号。     

复杂阵地米波雷达低仰角测高算法研究

针对复杂阵地下的米波雷达低仰角测高问题,建立多径信号服从高斯分布源的阵列信号模型,提出一种基于多维交替投影的合成导向矢量最大似然算法。该方法将回波信号的相关矩阵,在各个模型参数中交替投影,该方法可以同时对直达波角度和多径信号分布源参数进行精确的估计,并可以有效地对低仰角低空目标进行高度测量。与现有的测高算法相比,该方法在复杂阵地下能有效进行目标高度估计。计算机仿真结果和实测数据处理结果证实了该算法的可行性。     

面向一体化应用的电磁信号智能检测方法研究

针对复杂电磁环境中信号检测受限于低信噪比的问题,基于信号与噪声一体化的思路,提出了一种以电磁空间的所有电磁辐射信号为背景,并结合深度学习算法的电磁信号检测方法。首先建立动态场景的电磁环境模型,包括了通信基站信号、雷达信号、干扰信号等,其次使用加高斯窗傅里叶变换提取电磁信号时频域的能量分布特征,最后采用卷积神经网络进行特征选择分类,实现信号检测。仿真结果表明,该方法在一定程度上减轻了信号检测受限于信噪比的问题,克服了传统能量检测方法和基于SVM检测方法的缺陷,提高了低信噪比下电磁信号的检测性能。