Local Correlated Noise Improvement of Signal-to-Noise Ratio Gain in an Ensemble of Noisy Neuron

We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.

A NOVEL METHOD FOR ESTIMATING THE SNR OF UNKNOWN EMITTER SIGNAL

It is important to estimate the Signal-to-Noise Ratio(SNR) of unknown emitter signal accurately.In order to resolve the disadvantages of present algorithm,a novel method is proposed in this letter.We extract and normalize the information of zero frequency of received signal by the Wigner-Vile Distribution(WVD) transformation and then get the approximate power of original signal by mathematic transformation,at last,we get the estimate value of SNR by the known account formula of SNR.Simulation results show that it is correct and feasible.

A BLIND SNR ESTIMATOR FOR DIGITAL BANDPASS SIGNALS

A subspace-based blind Signal-to-Noise Ratio (SNR) estimation algorithm for digital bandpass signals in Additive White Gaussian Noise (AWGN) channel is discussed. The lower bounds of the mean and variance of the estimation are derived, and simulations are performed for the commonly used digital bandpass signals, such as MPSK (M=2, 4, 8), MFSK (M=2, 4) and MQAM (M=16, 64, 128, 256) signals. Theoretical analyses and simulation results indicate that the proposed algorithm is ef- fective even when the SNR is below 0dB. Furthermore, the algorithm can provide a blind estimator in that it needs neither the parameters of the received signals, such as the carrier frequency, symbol rate and modulation scheme, nor the synchronization of the system.

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

Nonsaturation Throughput Enhancement of IEEE 802.11b Distributed Coordination Function for Heterogeneous Traffic under Noisy Environment

In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.

红外遥感相机成像参数优化研究

为了更好地优化红外遥感相机成像参数,文章首次提出信噪比-动态范围积相机性能综合评价指标。建立了红外遥感相机的噪声电压模型并深入分析噪声影响,当恒定噪声电压项远大于时变噪声电压项时噪声电压模型可以近似简化为线性模型。进而建模分析了积分电容和积分时间对信噪比和动态范围的影响,并首次提出信噪比-动态范围积作为相机性能评价的指标。信噪比-动态范围积随着积分时间同步增大,积分时间较小时增速较快,积分时间较大时增速迅速下降;信噪比-动态范围积随着积分电容的增大先增加后减小,存在极值。研究结果对红外遥感相机的参数优化设计具有重要作用。

自适应动态滤波网络地震随机噪声压制方法

由于地质及环境条件复杂,导致地震勘探采集资料信噪比相对较低,对后续的研究带来不利影响,因此地震勘探数据处理中对随机噪声的压制一直备受关注。现有算法无法较好实现对噪声的有效压制和对有效信号的极大保留,为此,将传统方法和深度学习相结合,提出了基于自适应动态滤波网络(Adaptive Dynamic Filtering Net,ADFNet)的方法压制地震资料中的随机噪声。该网络以编码器—解码器为架构,首先引入通道注意力机制(Attention Mechanism,AM)的思想,通过通道AM对空洞卷积多尺度数据特征集成,为网络提供了精准且丰富的特征表示;然后,引入动态卷积,以较低的计算复杂度实现对地震资料高频特征的学习,从而保留更丰富的细节信息。合成数据和实际数据的实验结果均表明,ADFNet可有效压制地震资料中的随机噪声,同时保留更丰富的地震数据细节,处理后的地震数据信噪比得到显著提升。

低信噪比下基于深度学习TCNN-MobileNet的调制识别

将深度学习算法应用于调制识别任务是近年来通信领域的一个研究热点,但现有方法存在网络复杂度高、硬件要求高、在低信噪比(SNR)下识别准确率不高等问题。结合离散小波变换方法,提出一种基于双路卷积神经网络级联可分离卷积网络(TCNN-MobileNet)的调制识别方法。首先通过小波变换对数据进行预处理,将信号作为输入传送到双路卷积神经网络中进行不同维度的特征提取;然后通过融合层进行特征融合并送入轻量级神经网络MobileNetV1中,进行调制识别模型训练;最后通过全连接层进行11种调制识别的分类输出。在公开数据集RML2016.10a上的实验结果表明,在-20dB的低SNR下TCNN-MobileNet的识别准确率可达88.71%,在18dB的高SNR下识别准确率可达96.66%,SNR在-20~18dB范围内时平均识别准确率为88.37%,相比于ResNet18、ResNet34等经典网络架构提升了约35%。TCNN-MobileNet识别方法在保证识别精度不变的情况下能够降低训练参数量以及网络训练时间,有效简化网络架构,降低对硬件设备的要求,对轻量级神经网络在调制识别中的应用具有借鉴意义。

基于FRFT的低信噪比LFM信号参数快速估计算法

基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。

简易烈度计记录的噪声水平评价:以泸定M_(S)6.8级地震为例

在我国地震预警和烈度速报系统中,烈度计被广泛使用。烈度计的频谱特征主要是通过检定实验室进行标定,但在破坏性地震中,烈度计响应特点缺少必要的研究分析。该文以2022年四川泸定M_(S)6.8级地震为例,对比分析了烈度计记录和强震仪记录在噪声水平、信噪比(signal-to-noise ratio,SNR)、高通滤波截止频率(f_(HP))取值以及有效周期范围(T_(max))方面的特点,综合评价了烈度计记录的频谱特征,分析了噪声影响。得到以下结论:在整个频域范围内,烈度计记录的SNR要比强震仪记录平均意义上低一个数量级,可以估计烈度计动态范围较强震仪大致低20 dB;烈度计记录的最佳f_(HP)取值相较于强震仪记录存在更大的不确定性,尤其是在竖直方向上;烈度计记录的最佳f_(HP)取值更大,反应谱T_(max)较传统强震仪记录在水平方向小1~2 s,在竖直方向小3 s左右。该研究结果有助于合理应用烈度计记录,服务地震学和地震工程学相关研究。

GEO卫星机会源近岸海面高度测量性能评估

基于同步地球轨道(Geostationary Earth Orbit,GEO)卫星的机会源技术已被证明其在近岸海面高度测量中的应用潜力,但目前研究主要围绕可行性论证展开,极少开展全方位的测量性能评估.为此,从仿真和试验两个角度评估了信号带宽、非相干累加次数、信噪比及波形峰值跟踪算法对GEO卫星机会源海面测高性能的影响.评估结果表明当非相干累加次数和信噪比大于最优非相干累加次数和最优信噪比时,测高性能将不再显著提升.因此,通过选择最优非相干累加次数,以及设计合理的接收天线增益、射频增益及接收机带宽使信噪比大于最优信噪比对GEO卫星机会源海面测高至关重要.通过对比插值和拟合两种波形跟踪算法发现拟合法能提供比插值法更高的测量精度.利用北斗B3I信号和ASTRA 19.2 E卫星信号开展GEO卫星海面测高的试验评估.试验结果表明:在非相干累加次数大于10000、信噪比大于7 dB时,北斗B3I信号和ASTRA 19.2 E卫星信号可分别提供精度约为0.20 m和0.10 m的海面高度.针对GEO卫星多频或多通道传输特点,提出了基于信号频谱合成提高海面测高精度的方法.当利用中卫1号卫星10个通道信号进行合成时,插值法和拟合法的仿真测距精度分别为3.50 cm和0.69 cm.

基于改进小波阈值的数据采集系统降噪研究

在数据采集过程中,噪声和干扰的存在严重影响了数据的准确性。为了获得质量较高的数据,减少噪声的影响,该文提出一种基于遗传算法的小波阈值函数去噪方法,并设计以FPGA为主控的数据采集系统进行了多次实验。实验结果表明,经改进算法处理过的数据信噪比达到了芯片手册中所规定的典型值大小。同时,相比传统的阈值函数去噪方法,将新算法应用于采集到的数据,其信噪比提高了7.0248 dB,均方根误差降低了0.1675,表现出了较好的去噪性能,具有较高的工程应用价值。

Starlink卫星光学观测可见性分析

以Starlink为首的巨型星座快速发展对空间安全、天文观测等方面造成巨大影响,急需对其进行有效监测,光学观测是最有效的观测手段。根据Starlink在轨和计划发射的全部42000颗卫星的轨道信息和卫星结构特征建立了卫星轨道模型和光学反射模型,模拟了不同地区在不同季节和夜晚不同时间的卫星视星等分布;根据光电系统对Starlink卫星观测的成像特性建立了成像信噪比模型,模拟了50mm口径光电系统在不同地区、不同季节和夜晚不同时间观测Starlink卫星的成像信噪比分布,并用50mm口径图像全站仪进行观测实验验证。结果表明测站纬度越高,Starlink卫星夜间可观测时间越长,夏季可观测时间最长、冬季最短,在高纬度地区夏季整夜可见;南北纬40°附近可观测到卫星数量最多,为最佳观测位置;卫星越接近测站天顶位置视星等亮度越高,光电系统进行观测时成像信噪比也越高。

基于自适应估计SNR的分组Turbo码译码算法

针对分组Turbo码自适应Chase译码算法中SNR是预先设定值的情况,提出一种新的自适应估计信噪比(SNR)的译码算法。该方案利用了接收码字的统计信息与SNR之间存在的对应关系来调整门限函数,达到控制译码复杂度的目的。仿真结果表明,所提出的译码算法能自适应反映信道情况,降低译码运算复杂度,提高译码处理速度,获得了较好的性能。

基于逆全极点滤波器的高频PWM噪声整形方法

高频开关功率放大器具有高功率、低纹波、高响应的优点,是高加速、高精度定位系统的核心部件。为了解决高频PWM中开关频率与占空比分辨率的矛盾,该文分析调制中噪声整形的基本原理,采用误差反馈结构提取PWM量化噪声进行数字整形,阐述噪声整形滤波器设计约束条件。提出通过逆全极点无限脉冲响应(IIR)滤波器获得有限脉冲响应(FIR)噪声整形滤波器的简明设计方法,仿真及实验表明,该文提出的方法可利用低阶FIR整形滤波器获得平坦的通带和高整形衰减。在不足9位的PWM中,4阶的FIR噪声整形滤波器能够恢复调制信号在10 kHz频段内约40 dB的信噪比损失。

基于多普勒信号校正和短步长波束形成的列车轴承故障检测

针对受多普勒调制的移动声源信号时域幅值畸变、频域频带展宽,且在强背景噪声下信噪比(SNR)低,难以提取其故障特征等问题,提出了一种基于多普勒信号校正和短步长波束形成的列车轴承故障检测方法。首先,由声源和麦克风的相对运动关系和莫尔斯声学理论,得到了插值时间序列和幅值还原公式,对受多普勒调制的畸变信号进行了恢复;然后,提出了基于麦克风阵列的短步长波束形成方法,分段截断了信号,并对每段信号在其期望方向上进行了增强;最后,通过仿真研究了不同信噪比情况下该方法的有效性,设计了轨边故障检测实验,对产生内圈裂纹缺陷的滚动轴承进行了实验分析,并结合仿真与实验,对该方法的有效性进行了验证。研究结果表明:相较于8个麦克风收集的原始信号,信号时域、频域均被校正,且信噪比增加了11.5 dB,在强噪声背景下,采用该方法能够恢复多普勒畸变信号,并有效提取移动声源的故障特征。

卫星升降轨对GNSS-IR海面高度反演的影响

针对在基于信噪比数据的全球卫星导航系统干涉反射测量(GNSS-IR)海面高度反演中,反演精度受多种因素影响,因卫星信号需要连续跟踪的特点,可能导致在低卫星高度角处的升轨与降轨信噪比数据质量不同的问题,探究卫星升降轨对GNSS-IR海面高度反演的影响:给出GNSS-IR原理,并分析卫星升降轨影响;然后通过SC02、AT01测站开展研究。实验结果表明,卫星升降轨的不同会直接导致信噪比振荡效果不同;降轨数据反演结果明显优于升轨数据;2测站因观测环境与设备不同,升降轨的影响也不同,SC02站降轨数据反演精度比升轨数据提升5 cm以上,AT01站降轨数据反演精度比升轨数据提升3 cm以上。因此,在处理GNSS-IR海面高度多频多模反演数据时,卫星升轨与降轨的影响不容忽视。