Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation

Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

Comparison of signal reconstruction under different transforms

A newalgorithm, called Magnitude Cut, to recover a signal from its phase in the transform domain, is proposed.First, the recovery problem is converted to an equivalent convex optimization problem, and then it is solved by the block coordinate descent（ BCD） algorithm and the interior point algorithm. Finally, the one-dimensional and twodimensional signal reconstructions are implemented and the reconstruction results under the Fourier transform with a Gaussian random mask（ FTGM）, the Cauchy wavelets transform（ CWT）, the Fourier transform with a binary random mask（ FTBM） and the Gaussian random transform（ GRT） are also comparatively analyzed. The analysis results reveal that the M agnitude Cut method can reconstruct the original signal with the phase information of different transforms; and it needs less phase information to recover the signal from the phase of the FTGM or GRT than that of FTBM or CWT under the same reconstruction error.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks （WSNs）, a new signal reconstruction algorithm based on a cubature Kalman particle filter （CKPF） is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter （CKF） to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter （UKPF） and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

Extracting useful high-frequency information from wide-field electromagnetic data using time-domain signal reconstruction

The wide-field electromagnetic method is widely used in hydrocarbon exploration,mineral deposit detection,and geological disaster prediction.However,apparent resistivity and normalized field amplitude exceeding 2048 Hz often exhibit upward warping in data,making geophysical inversion and interpretation challenging.The cumulative error of the crystal oscillator in signal transmission and acquisition contributes to an upturned apparent resistivity curve.To address this,a high-frequency information extraction method is proposed based on time-domain signal reconstruction,which helps to record a complete current data sequence;moreover,it helps estimate the crystal oscillator error for the transmitted signal.Considering the recorded error,a received signal was corrected using a set of reconstruction algorithms.After processing,the high-frequency component of the wide-field electromagnetic data was not upturned,while accurate high-frequency information was extracted from the signal.Therefore,the proposed method helped effectively extract high-frequency components of all wide-field electromagnetic data.

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.

Obtaining Prior Information for Ultrasonic Signal Reconstruction from FRI Sparse Sampling Data

Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal sampling rate and the quantity of sampling data.However,the pulse number of the signal must be known beforehand for the signal reconstruction procedure.The accuracy of this prior information directly affects the accuracy of the estimated parameters of the signal and influences the assessment of flaws,leading to a lower defect detection ratio.Although the pulse number can be pre-given by theoretical analysis,the process is still unable to assess actual complex random orientation defects.Therefore,this paper proposes a new method that uses singular value decomposition(SVD) for estimating the pulse number from sparse sampling data and avoids the shortcoming of providing the pulse number in advance for signal reconstruction.When the sparse sampling data have been acquired from the ultrasonic signal,these data are transformed to discrete Fourier coefficients.A Hankel matrix is then constructed from these coefficients,and SVD is performed on the matrix.The decomposition coefficients reserve the information of the pulse number.When the decomposition coefficients generated by noise according to noise level are removed,the number of the remaining decomposition coefficients is the signal pulse number.The feasibility of the proposed method was verified through simulation experiments.The applicability was tested in ultrasonic experiments by using sample flawed pipelines.Results from simulations and real experiments demonstrated the efficiency of this method.

Reconstruction of sub cross-correlation cancellation technique for unambiguous acquisition of BOC(kn, n) signals

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.

RECONSTRUCT AZIMUTH SIGNAL AND SUPPRESS INTERBEAM AMBIGUITIES OF SPCMB SAR WITH HYBRID FILTERBANK

Conventional Synthetic Aperture Radar (SAR) systems cannot obtain high-resolution and wide-swath illumination area due to the well-known minimum antenna area constraint. Single Phase Center MultiBeam (SPCMB) technique can overcome this limitation by adding spatial sampling through multiple receivers in azimuth direction. Unfortunately, this approach will lead to an increase of azimuth ambiguities (interbeam ambiguities), because each receive beam’s mainlobe overlaps with the other ones’ sidelobes. This paper proves that the front part of SPCMB SAR systems can be considered to be a hybrid filterbank. Therefore, the azimuth signal can be reconstructed and the interbeam am- biguities can be effectively suppressed by a well-designed hybrid filterbank.

RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

Application of Sensitivity Encoding Reconstruction for MRI with BOLD Signal

The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The actual data set handles the image resolution. The performance and experimental results of SENSE are analyzed through actual data sets.

基于小波变换的锚杆锚索测力仪设计

针对井下存在的电磁干扰,会对测力仪采集到的运放端的电压信号存在较大的干扰,进而对传感器测得的压力值准确性产生影响的问题;设计了基于小波变换的锚杆锚索测力仪。以国产HC32L176单片机作为主控芯片,对运放端的电压信号进行AD采集;选用合适的小波基函数、分解层数、阈值规则及阈值函数,对含噪信号进行分解,滤除不同层的高频成分,再对信号重构,得到去噪后的信号。Matlab仿真与实际测试结果表明:小波变换可以对采集的电压信号实现很好去噪,提高了锚杆锚索测力仪的可靠性与稳定性。

SELECTION OF PROPER EMBEDDING DIMENSION IN PHASE SPACE RECONSTRUCTION OF SPEECH SIGNALS

In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.

基于串并行双分支网络的冲击波信号重构方法

通过有限测点数据重建冲击波场内压力分布、通过残缺数据重构完整的冲击波压力曲线,对武器威力以及目标毁伤评估具有重要价值。针对爆炸冲击波信号重构问题,建立Res-GRU分支以串行方式捕捉冲击波超压信号局部时序依赖关系;建立Transformer分支以并行方式分析信号全局潜在特征;建立特征融合单元进行高阶特征融合,实现不同阶段信息逐层互补;进而构建了基于门控循环单元(gated recurrent unit,GRU)和Transformer模型的串并行双分支网络(G-TNet)。试验研究表明:G-TNet综合考量了信号的时序关系、数据变化规律等特征信息;在基于有限测点数据的冲击波场压力分布重构试验中,重建的模拟、实测超压数据与原始值之间均方误差(mean square error,MSE)分别为5.0×10^(-6)、1.2×10-3,平均峰值误差分别为0.49%、27.01%,平均正压时间误差分别为15.62%、15.91%,平均比冲量误差分别为17.66%、19.33%;在基于残缺数据的冲击波压力曲线重构试验中,重构的模拟、实测信号的缺失值与原始值之间MSE分别为5.0×10^(-6)和5.0×10^(-4),平均绝对误差(mean absolute error,MAE)分别为0.0010和0.0171;G-TNet重构结果优于主流方法,满足爆炸冲击波压力重构指标要求。

基于自适应参数估计的微动时频表征重构方法

针对数据缺失条件下的目标微动回波时频表征重构问题,提出了一种基于自适应参数估计的微动时频表征重构方法。首先,将缺失的微动时频表征重构问题建模为基于L_P范数最小化的稀疏重构问题,其次,引入哈达玛积参数将L_P范数最小化稀疏重构问题转化为多个L_2范数联合最小化问题,并采用迭代吉洪诺夫正则化求解,同时在每次迭代过程中根据重构结果自适应估计正则化参数,最后,采用除偏处理减小了重构时频表征的振幅衰减。与传统微动回波时频表征重构方法相比,所提方法避免了需要人工设置正则化参数不足的问题,并且重构的时频表征更加完整。仿真实验和实测数据处理结果验证了所提方法的有效性和稳健性。

基于决策边界敏感性和小波变换的电磁信号调制智能识别对抗样本检测方法

深度学习在图像分类和分割、物体检测和追踪、医疗、翻译和语音识别等与人类相关的任务中取得了巨大的成功。它能够处理大量复杂的数据,并自动提取特征进行预测,因此可以更准确地预测结果。随着深度学习模型的不断发展,以及可获得的数据和计算能力的提高,这些应用的准确性不断提升。最近,深度学习也在电磁信号领域得到了广泛应用,例如利用神经网络根据信号的频域和时域特征对其进行分类。但神经网络容易受到对抗样本的干扰,这些对抗样本可以轻易欺骗神经网络,导致分类错误。因此,对抗样本的生成、检测和防护的研究变得尤为重要,这将促进深度学习在电磁信号领域和其他领域的发展。针对现阶段单一的检测方法的有效性不高的问题,提出了基于决策边界敏感性和小波变换重构的对抗样本检测方法。利用了对抗样本与正常样本对模型决策边界的敏感性差异来进行检测,接着针对第一检测阶段中未检测出的对抗样本,本文利用小波变换对样本进行重构,利用样本去噪前后在模型中的预测值差异来进行检测。本文在两种调制信号数据集上进行了实验分析,并与基线检测方法进行对比,此方法更优。这一研究的创新点在于综合考虑了模型决策边界的敏感性和小波变换的重构能力,通过巧妙的组合,提出了一种更为全面、精准的对抗样本检测方法。这为深度学习在电磁信号领域的稳健应用提供了新的思路和方法。

基于傅里叶级数的光栅波长信号重构及振动位移精准测量

为实现光纤光栅传感器在多频率振动位移中的精确测量,研究了一种基于傅里叶级数的光栅波长转换。该方法将光栅波长偏移量按照振动频率分解,依据波长变化量与加速度的关系式得到振动频率分量产生的振动加速度,通过对振动加速度进行二次积分得到振动位移,对振动位移求和,实现整体振动位移的精确测量。实验结果表明,在检测单频率振动位移时,该方法的最大误差率为3.74%,基于振动主频的光栅波长转化方法的最大误差率为-4.60%。在检测双频率振动位移时,该方法的最大误差率为-8.45%,基于振动主频的光栅波长转化方法的最大误差率为-74.25%。因此,该基于傅里叶级数的光栅波长转化方法能够准确分解并重构光栅波长信号,在测量多频率振动位移时精度更高,稳定性更强。

基于短源声波管道的鼻腔三维结构重塑方法

声波反射技术是一种非侵入性测量技术,并且可通过回传的反射波构造物体内部结构,所以在医学测量鼻腔结构方向有着天然优势。然而,该技术中的长源声波管不便于携带以及成本较高的问题制约了该方法在医学方向的应用。因此,本研究提出了基于短源声波管道的鼻腔三维结构重塑方法以解决这些问题。本研究通过分析短源声波管道中的脉冲声波传播特性,解决了短源声波管道分离入射波与反射波困难的问题;并且采用了校正直流偏移分量和引入约束因子抑制高频噪声的方法对鼻腔声波信号进行预处理;最后利用鼻腔三维结构分层重塑方法重塑出鼻腔结构二维曲线并进行了三维可视化。实验结果表明:约束因子对重塑鼻腔结果有较大的影响,本文确定了约束因子的最佳取值范围。