Optimal signal design strategy with improper Gaussian signaling in the Z-interference channel

本文提出一种用于在干扰被视为加性高斯噪声的假设下在Z干扰信道(z-interference channel,Z-IC)上实现非正则高斯信号(IGS)的Pareto边界(可实现速率区域的边界)。具体来说,文章表明了帕累托边界可由某个用户的传输速率的两个区域表征,由传输信号的方差与伪协方差的两个变化路径决定。为每个用户发送的非正则信号的最优协方差和伪协方差设计推导出简明闭合的表达式。为此,我们通过仿真试验有力证明了所提出的最优信号设计策略的有效性,同时直观地展现出IGS较正则高斯信号(proper Gaussian signaling,PGS)的优越性。此外,我们提出的最优信号设计策略提供了实现所需速率区域的简单方法。利用该方法,我们得到了一个闭合形式的解决方案,用于快速确定最大和速率的信号设计方案。最后,我们深入讨论了帕累托边界的特征,定性分析了信道系数以及两个用户发送的信号的协方差和伪协方差与其的关系。

基于模态理论和改进GMM的声发射源识别研究

基于模态声发射信号理论,提出了一种利用声学对数倒谱统计参数作为声发射信号特征参数的分析与提取方法。从声发射信号多模态特性出发,提出了一个基于改进高斯混合模型的声发射源信号识别系统。理论分析和实验结果表明,该方法能准确地判断声发射信号源,不仅能够应用于突发型声发射信号的识别,而且可以应用于连续型声发射信号的识别。

基于三角函数的核信号类高斯成形算法研究

高斯信号具有高信噪比、抗弹道亏损和易于提取幅度等优点。因此,在核辐射测量系统中,核信号的高斯成形被广泛应用于探测器输出信号的滤波和幅度提取。然而,目前实时高斯成形核脉冲信号仍然具有挑战性。常见的Sallen-Key滤波器和CR-(RC)^(m)滤波器输出的类高斯信号还存在对称性差和下冲等问题。本文提出了一种基于三角函数的双指数信号类高斯脉冲成形算法。该算法以三角函数为基础,通过平移和反转可得到左右对称的类高斯信号(1-cosx),采用Z变换方法推导了该算法的数字递推式。仿真和实测结果表明:在相同达峰时间条件下,相较于正弦函数(sin)脉冲成形,类高斯脉冲成形具有更好的滤波性能,信噪比(SNR)提升了8.95%。同时,类高斯脉冲成形所得能谱的能量分辨率较sin脉冲成形更优。与梯形脉冲成形相比,类高斯脉冲成形具有更好的堆积脉冲分离能力,展现出良好的应用前景。

混合半径高斯滤波算法在去除GRACE条带误差中的应用

从GRACE重力卫星获取的时变重力场存在严重的南北条带误差,极大地掩盖了真实重力场信号,因此需要进行滤波处理。以GRACE相关滤波理论为基础,以信噪比最优为评估依据,详细分析不同高斯滤波半径在各阶次下信噪比以及高斯权重系数变化情况。基于此,提出混合半径高斯滤波方法,即通过调整各阶次下的高斯滤波半径来优化高斯滤波权重系数。结果表明,在仅进行400 km经典高斯滤波、去相关滤波与300 km经典高斯滤波的组合滤波信噪比最优情况下,采用分两步进行的400 km混合半径高斯滤波、去相关滤波与分3步进行的300 km混合半径高斯滤波方法,能进一步提高信噪比,同时改善因高斯滤波造成的信号泄漏。

基于自适应极值的白光干涉信号解调算法研究

针对传统时域解调算法等光程点定位稳定性差、抗干扰能力弱,而频域解调算法效率低且部分方法受扫描采样步长限制等问题,课题组提出一种基于自适应极值的白光干涉解调算法。从干涉信号统计学规律出发,通过对干涉信号包络曲线加权平均,将它修正为高斯型包络曲线;然后,通过对包络曲线高斯拟合插值得到等光程点,进而得到测量方向的深度信息以及三维形貌;最后,以高度为20μm标准台阶样块为测量对象进行多次实验。结果表明:与传统算法相比,课题组所提出算法的测量重复性和精度均有显著提升。该算法为白光干涉中信号解调提供了一种重复性更好、误差更小的方法。

基于GA-MP的低复杂度OTFS检测算法

针对正交时频空(Orthogonal Time Frequency Space,OTFS)通信系统信号检测复杂度高的问题,提出一种改进的高斯近似消息传递(Gaussian Approximate Message Passing,GA-MP)检测算法。依据最大后验概率检测准则,对发送信号及隐变量进行逐符号高斯近似,基于置信传播算法与联合因子图进行消息传递,用边缘后验概率替代GA-MP中的外部信息以减少运算量,结合阻尼因子提升收敛速度,同时引入概率阈值减少后续更新的节点数,从而使运算复杂度得到有效降低。实验结果表明,改进后的GA-MP算法在保证误码率性能的前提下具有更低的复杂度。

基于多站数据融合的参数精估计方法

针对侦察设备处于星载SAR副瓣照射范围,从而导致截获信号湮没于强噪声背景这个问题,本文提出一种基于多站接收机之间的数据融合方法。在信号形式未知的情况下,通过此方法可以检测出淹没在噪声中的微弱信号,进行信号的分类和时频域参数的精估计。首先,将参考接收机与其他接收机之间进行互相关处理,得到峰值信息,根据峰值信息的位置得到信号与参考信号之间的延迟位置,进行延迟校准;其次,各个接收机分别进行粗步长的分数阶傅里叶变换(Fractional Fourier Transform,FrFT),记录峰值信息为精估计做准备,根据峰值角度和分数阶傅里叶反变换恢复出原始信号;最后,判定是否存在信号,若信号存在实现多站原始信号功率比的加性融合,根据多站峰值信息限定旋转角度范围,采用精步长的分数阶傅里叶变换估计出调频率和中心频率;利用联合互相关谱实现信号能量的累积,采用自适应门线和边界波谷连续取小方法,找到信号存续状态中的左右边界,估计出带宽和中心频率,计算脉宽,实现时频域信号的精估计。仿真实验表明:该方法可以在低信噪比的高斯白噪声和有色噪声背景下,对线性调频信号(Chirp)的时频参数进行有效的精估计。

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

Real-time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware

The sense of being within a three-dimensional （3D） space and interacting with virtual 3D objects in a computer-generated virtual environment （VE） often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit （GPU）. While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence （AI） and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

基于RSSI的井下人员定位算法改进

在无线传感器网络中,针对接收信号强度指示(Received Signal Strength Indication,RSSI)在煤矿井下长距离巷道内信号衰减快、测距精度偏差大等问题,提出了一种基于RSSI的高斯滤波加权质心定位算法。采用高斯滤波对采集的RSSI值进行修正,一定程度上减轻环境造成的影响。将RSSI测距算法与改进加权质心算法相结合,得出待测节点坐标位置。仿真试验表明,该改进算法与原有定位算法相比,定位误差明显降低,可基本满足煤矿井下人员的安全生产和定位需求。

Effect of Asymmetric Potential and Gaussian Colored Noise on Stochastic Resonance

The phenomenon of stochastic resonance (SR) in a bistable nonlinear system is studied when the system is driven by the asymmetric potential and additive Gaussian colored noise. Using the unified colored noise approximation method, the additive Gaussian colored noise can be simplified to additive Gaussian white noise. The signal-to-noise ratio (SNR) is calculated according to the generalized two-state theory (shown in [H.S. Wio and S. Bouzat, Brazilian J.Phys. 29 (1999) 136]). We find that the SNR increases with the proximity of a to zero. In addition, the correlation time τ between the additive Gaussian colored noise is also an ingredient to improve SR. The shorter the correlation time τ between the Gaussian additive colored noise is, the higher of the peak value of SNR.

Numerical Studies of the Generalized <i>l</i>₁Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.

基于B样条加权三重叠加的随机振动信号生成技术研究

随机振动试验是评估工程结构在高斯振动载荷力学环境下的安全性和可靠性的一种模拟试验,而随机振动控制功率谱密度生成时域高斯随机振动驱动信号是随机振动试验关键技术之一,常用的生成高斯驱动信号方法主要是伪随机法、正弦窗叠加法等。本文提出了基于B样条加权三重叠加的随机振动信号生成方法,并与常用正弦窗叠加法进行了对比,结果表明:基于B样条加权三重叠加的随机振动信号生成方法提高振动模拟试验的真实性和控制幅值动态范围精度,而这两种方法生成时域随机信号的偏斜度、峭度值相当,都满足高斯分布性能要求,验证了该新方法的正确性和有效性。

Stochastic resonance in a single-mode laser driven by frequency modulated signal and coloured noises

By adding frequency modulated signals to the intensity equation of gain noise model of the single-mode laser driven by two coloured noises which are correlated, this paper uses the linear approximation method to calculate the power spectrum and signal-to-noise ratio （SNR） of the laser intensity. The results show that the SNR appears typical stochastic resonance with the variation of intensity of the pump noise and quantum noise. As the amplitude of a modulated signal has effects on the SNR, it shows suppression, monotone increasing, stochastic resonance, and multiple stochastic resonance with the variation of the frequency of a carrier signal and modulated signal.

Generation of Non-Gaussian Wide-Sense Stationary Random Processes with Desired PSDs and PDFs

This paper describes a new method to generate discrete signals with arbitrary power spectral density (PSD) and first order probability density function (PDF) without any limitation on PDFs and PSDs. The first approximation has been achieved by using a nonlinear transform function. At the second stage the desired PDF was approximated by a number of symmetric PDFs with defined variance. Each one provides a part of energy from total signal with different ratios of remained desired PSD. These symmetric PDFs defined by sinusoidal components with random amplitude, frequency and phase variables. Both analytic results and examples are included. The proposed scheme has been proved to be useful in simulations involving non-Gaussian processes with specific PSDs and PDFs.

Characteristic Analysis of White Gaussian Noise in S-Transformation Domain

The characteristic property of white Gaussian noise (WGN) is derived in S-transformation domain. The results show that the distribution of normalized S-spectrum of WGN follows X2?distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in S-transformation can be realized.

Splitting of Gaussian Models via Adapted BML Method Pertaining to Cry-Based Diagnostic System

In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.

Implementation of a Novel Algorithm on Acoustic Doppler Current Profiler Signal Processing System

Acoustic Doppler current profiler （ADCP） uses acoustic energy directed along narrow beams for current measurement. In conventional method, the quantity of sampling affects the precision of fast Fourier transform （FFT） algorithm, and the algorithm needs a large amount of data to process. A novel frequency estimator,enhanced least mean square （ELMS） algorithm for a single complex sinusoid in complex white Gaussian noise, is proposed in ADCP system. As sampling frequency equals 120 krad/s and the sampling number equals 240, the minimum resolving is 0.5 krad/s. All variances keep 11.1100. ELMS algorithm needs less data than FFT. And the robust algorithm can estimate the spectrum true value to 99.900 when the signal to noise ratio （SNR） is equal to 0 dB. Experiments prove that the estimation values will diverge much from the ideal when SNR is less than -6 dB.

CFAR DETECTORS FOR HIGH RESOLUTION RADAR IN NON-GAUSSIAN CLUTTER

讨论了球不变随机过程（ＳＩＲＰ）非高斯杂波背景下高分辨雷达距离扩展目标检测问题。文中提出了ＣＡ－ＣＦＡＲ，ＯＳ－ＣＦＡＲ两种距离扩展目标检测器，并证明了它们的形状因子和尺度因子未知的情形下具有恒虚警性，即双参数恒虚警性，分析了这两种检测器的检测性能。这两种检测器亦可用作ＳＩＲＰ杂波背景下低分辨雷达检测器。