Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Modeling a Periodic Signal Using Fourier Series

This paper covers the concept of Fourier series and its application for a periodic signal. A periodic signal is a signal that repeats its pattern over time at regular intervals. The idea inspiring is to approximate a regular periodic signal, under Dirichlet conditions, via a linear superposition of trigonometric functions, thus Fourier polynomials are constructed. The Dirichlet conditions, are a set of mathematical conditions, providing a foundational framework for the validity of the Fourier series representation. By understanding and applying these conditions, we can accurately represent and process periodic signals, leading to advancements in various areas of signal processing. The resulting Fourier approximation allows complex periodic signals to be expressed as a sum of simpler sinusoidal functions, making it easier to analyze and manipulate such signals.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

Observation 20-s periodic signals on Mars from InSight,Sols 800-1,000

Seismometers of the InSight probe(Interior Exploration using Seismic Investigation,Geodesy and Heat Transport)currently operating on Mars have recorded not only seismic events but also high-frequency non-seismic periodic signals that appear to have been induced by variations in the Martian environment and the hardware.Here,we report an observation of a long-period signal with a dominant period of~20 s from Martian solar days(Sol)800 to Sol 1,000.This 20-s signal is detected mostly at quiet nighttime—from22:00 to 04:00 LMST(Local Mean Solar Time)—at the InSight landing site.The measurement of the particle motion suggests that this linearly polarized signal focuses on the horizontal plane with an angle of~30°from the north.By examining the temporal variation of the signal’s amplitude and polarization angle and its times of occurrence in relation to the planet’s atmospheric data,we suggest that this20-s signal may be relevant to wind and temperature variations on Mars.Furthermore,we study the possible influence of this 20-s signal on the noise autocorrelation and find that the stacked autocorrelograms can be quite different when the 20-s signal is present.

Joint observation of long period tremor signals with broadband seismometer,tiltmeter and gravimeter

We report here the observation result of joint observation of long period tremor signals with broadband seismome-ter,tiltmeter and gravimeter at the HUST(Huazhong University of Science and Technology)station.The observed data were compared and analyzed.Since 2005,the several tens of abnormal tremor signals which are weak,com-plex and duration of 2 to 3 days have been synchronously recorded by the different instruments.The tremor signals have the periodic domain in the range of 3 to 5 minutes,20 to 30 minutes and even more than 1 hour.The observa-tion shows such tremors are a physical existence.The analysis indicates that a part of the tremors caused by the typhoon from the western Pacific Ocean.These tremors have a close relationship with wind velocity of typhoon and distance between the typhoon center and the station.Except these,the cause of others is still unclear.

Research and application of regular phenomenon between periodic signals

The phase change between periodic signals is regular. Research on the regular phenomenon between periodic signals is helpful to improve the precision of some measurements and develop some new measurement methods. So it is necessary to analyze the characteristics of the greatest common factor frequency and the least common multiple period universally existing in periodic signals. The regulation of the quantitative phase shift resolution between periodic signals is presented.The cause of difference in phase characteristics between periodic signals is explained well. In this paper we propose different application prospects based on the regular phenomenon between periodic signals, with focusing on the methods for high precision frequency measurement and transient stability measurement. The experimental results are satisfactory.

Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal

This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

DETECTING TECHNIQUE OF WEAK PERIODIC PULSE SIGNAL VIA SYNTHESIS OF CROSS-CORRELATION AND CHAOTIC SYSTEM

In this letter, with the synthesis of usual cross-correlation detecting method andchaotic detecting method, a new detecting system for the weak periodic pulse signal is constituted,in which the two methods can play respective preponderance. Theoretical analyses and simulationstudies have shown that the detecting system is very sensitive to the periodic pulse signal understrong noise background and has exceedingly powerful capability of suppressing complex noise.

THE CHAOTIC DETECTION OF PERIODIC SHORT-IMPULSE SIGNALS UNDER STRONG NOISE BACKGROUND

The periodic short-impulse signals under strong noise background are successfully detected with a special chaotic system invented by the authors. Simulation experiments show that the chaotic system is very sensitive to periodic short-impulse signals submerged by strong noise background, and it can effectively restrain any zero-mean noise. The system has a stable working-detection limit of -83dB.

LOOP-SHIFT S&G ADAPTIVE FILTER AND ITS APPLICATION IN SEPARATION OF OVERLAPPED PERIODIC BIOMEDICAL SIGNALS

A Loop-Shift Stop & Go Adaptive Filter(LSSGAF) with applications in period detection and signal separation of overlapped periodic signals is presented. The filter places no constraints on the overlapped forms in frequency domain and requires no priori knowledge except for approximate period range of signals to be separated, therefore it provides for an efficient and feasible method to separate periodic signals overlapped both in time and frequency domains. The convergence conditions of the filter and the requirements of all parameter selections are discussed, meanwhile a rough scanning and channel auto-shut algorithm is proposed to reduce the amount of calculation in the searching stage. Experiments show that the filter can separate all kinds of overlapped periodic signals with different initial phases and different relative magnitudes.

North Atlantic Abrupt Climate Signals during the Last Glacial Period in Central Asia：Evidences from Aeolian Loess Sediments

Objective Climate fluctuations over suborbital or millennial timescale display significant instability during the last glacial period,which are often superimposed upon the orbital periodicity.They triggered some abrupt climate events,

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillator＇s phase trajectory in a small- scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system.

快速刀具伺服系统位置域重复控制设计及其数字实现

在非圆零件车削过程中,快速刀具伺服(Fast tool servo,FTS)的运动精度直接影响零件的加工质量.主轴变速加工使得FTS的参考目标信号周期时变而不确定,这对实现其渐近跟踪提出了极大的挑战.本文利用FTS的位置域周期特性,提出一种基于位置域重复控制和时域速度反馈镇定的FTS系统复合控制设计方法,并给出位置域改进型重复控制器(Spatial modified repetitive controller,SMRC)的数字实现算法,实现对时变周期参考目标信号的高精度跟踪.首先,建立包含位置相关时变周期参考目标信号内模的SMRC,并引入位置域相位超前装置对镇定补偿器引起的相位滞后进行补偿,在此基础上构建复合控制律.然后应用小增益定理和算子理论,推导出控制系统的稳定性条件,在保持系统采样频率不变的条件下,应用插值法建立SMRC的数字实现算法,确保位置域重复控制和时域镇定控制器的同步执行.最后,通过仿真验证所设计的FTS控制系统具有满意的时变周期跟踪性能和鲁棒性,并通过与其他位置域重复控制方法的比较,说明所提方法同时具有更好的暂态和稳态性能.

溯源弹簧形变过程的断路器振动信号递归量化分析辨识方法

从锁止机构脱扣引起储能弹簧释能,经部件带动动触头运动再到静止的每个动作具有严格阶段特征,伴随断路器动作的机械振动展现了能量传递及设备健康状态。该文提出一种溯源弹簧形变过程的断路器振动信号递归量化分析方法,首先由高速相机捕捉断路器操动时储能弹簧的动作图像,通过计算机视觉跟踪动态提取反映弹簧形变特征帧,再依据特征帧时序划分操动过程;然后将不同阶段振动信号映射至高维相空间,经递归分析得到体现动力系统变化特征的递归图,并递归量化分析其纹理结构特征;最后利用支持向量机模型对正常及故障状态下的断路器振动特征样本进行分析辨识,对比结果证明,由弹簧释能时序细化振动信号特征分析有效提高了分类识别准确率。该文方法在断路器操动机构状态辨识中具有广阔的应用前景。

脉动疲劳加载下振动信号的建模及损伤演化

围绕脉动加载下的疲劳损伤演化,基于振动信号的测量数据,应用非线性常微分方程建立周期振动信号的参数化建模与损伤演化检测方法。采用多项式逼近非线性函数的策略,给出非线性常微分方程模型的最小二乘辨识方法;针对测量数据中的噪声导致参数估计偏差的问题,采用谱分析算法剔除数据中的噪声干扰;基于辨识得到的非线性常微分方程模型,建立疲劳损伤的非线性特征参数;通过脉动疲劳试验,验证了所提出方法的有效性。

COPD急性加重期患者外周血单个核细胞SOCS-1、TLR4 mRNA及血清cTnT、尿酸水平变化分析

目的探讨慢性阻塞性肺疾病(COPD)急性加重期患者外周血单个核细胞中细胞因子信号抑制蛋白-1(SOCS-1)、Toll样受体4(TLR4)mRNA水平及血清心肌肌钙蛋白T(cTnT)、尿酸水平变化。方法收集COPD急性加重期(组)患者70例,COPD稳定期(组)患者40例,对照组健康志愿者40名。检测外周血单个核细胞SOCS-1、TLR4 mRNA水平及血清cTnT、尿酸浓度;行肺功能检查并记录相关指标(FEV1、FEV1%、FEV1/FVC%)。对COPD急性加重期患者进行1 a随访,分为预后不良组和预后良好组。对外周血单个核细胞SOCS-1、TLR4 mRNA水平、血清cTnT、尿酸浓度行Pearson相关性分析,并对COPD急性加重期患者预后评估价值进行ROC曲线分析。结果COPD急性加重期组SOCS-1 mRNA表达水平明显低于COPD稳定期组、对照组,TLR4 mRNA水平及血清cTnT、尿酸浓度明显高于COPD稳定期组和对照组(均P<0.01)。COPD急性加重期组FEV1、FEV1%、FEV1/FVC%明显低于COPD稳定期组和对照组(P<0.05)。COPD急性加重期患者FEV1/FVC%与外周血单个核细胞SOCS-1 mRNA表达水平呈正相关关系(P<0.01),与外周血单个核细胞TLR4 mRNA水平及血清cTnT、尿酸浓度呈负相关关系(P<0.01)。预后不良组SOCS-1 mRNA水平明显低于预后良好组,TLR4 mRNA水平及血清cTnT、尿酸浓度明显高于预后良好组(P<0.05)。外周血单个核细胞SOCS-1、TLR4 mRNA水平及血清cTnT、尿酸联合检测对COPD急性加重期患者预后具有较高的评估价值。结论COPD急性加重期患者SOCS-1低表达,TLR4、cTnT、尿酸高表达,且与肺功能水平密切相关,联合检测对患者预后具有较高的评估价值。

PSO算法下GP-周期随机共振的微弱OFDM信号检测

为了解决传统随机共振系统在微弱正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号解调应用中存在的人工选择参数困难、解调效果差的问题,提出一种基于粒子群优化(Particle Swarm Optimization,PWO)算法的GP(Gaussian Potential)-周期随机共振系统的微弱OFDM信号检测方法。该方法利用GP-周期随机系统对OFDM信号经正交变换后的两路高频信号分别进行随机共振,同时运用阻尼参数与信号频率之间的关系对两路信号进行降频处理,使得上下两路信号都能够发生随机共振;然后使用PWO算法对随机共振的系统参数进行优化处理,以使得上下两路信号达到最优的共振效果;最后,将上下两路共振之后的信号合成完成OFDM信号解调,得到最终的数字序列。本文探讨了优化前后GP-周期随机共振系统的共振效果,研究了优化前后GP-周期随机共振诱导下的OFDM系统星座图的聚集程度以及误码率情况,对比分析了经典双稳随机共振、经典三稳随机共振和所提模型对OFDM信号检测的影响。仿真结果表明:相比常用的传统随机共振模型,该方法用于OFDM信号检测的星座图中信号点的聚集程度更高,同时OFDM系统误码率在同样的信噪比下降低50%左右。

基于天线扫描特征分析的信号分选算法研究

针对传统分选算法难以对电磁参数交叠、方位相近的密集分布目标进行准确分选的难题,提出一种基于天线扫描特征分析的信号分选算法。首先利用辐射源天线扫描周期的差异性,将脉冲序列按照一定的单位进行量化,然后根据天线扫描周期的相关性提取属于同一辐射源的脉冲,最后根据主瓣时间间隔的一致性对目标进行确认,克服了传统分选算法对电磁参数和方位的依赖。仿真与实际数据验证表明,与传统分选算法相比,本算法可有效提升参数交叠、且方位密集分布环境下的信号分选能力,信号分选准确率可提升30%以上。

双通道周期信号分离装置的设计与实现

基于解决现代工业生产中复杂信号分析难题的目标,该研究采用了创新性的“双通道周期信号分离装置”。该装置在工业仪表、控制系统和传感器等领域广泛应用,能高效精准地分离多频率、多波形混合信号。面对工业环境中频率和波形多样的挑战,该研究结合STM32平台,成功实现对周期信号A和B的准确分离和还原。装置工作原理简明清晰,信号经过预处理模块消除直流成分后,经STM32智能核心板时域离散采样和傅里叶变换等处理,实现对信号频谱特征的识别。信号再构模块则采用频率相位可编程信号发生器,对信号进行再构,最终实现双通道周期信号的精确分离。经过实验测试,装置在叠加信号测试中表现出色,成功分离多种信号组合,验证了其高效性与精确性。