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.

Time-domain wavefield reconstruction inversion

Wave field reconstruction inversion （WRI） is an improved full waveform inversion theory that has been proposed in recent years. WRI method expands the searching space by introducing the wave equation into the objective function and reconstructing the wavefield to update model parameters, thereby improving the computing efficiency and mitigating the influence of the local minimum. However, frequency-domain WRI is difficult to apply to real seismic data because of the high computational memory demand and requirement of time-frequency transformation with additional computational costs. In this paper, wavefield reconstruction inversion theory is extended into the time domain, the augmented wave equation of WRI is derived in the time domain, and the model gradient is modified according to the numerical test with anomalies. The examples of synthetic data illustrate the accuracy of time-domain WRI and the low dependency of WRI on low-frequency information.

An application of matching pursuit time-frequency decomposition method using multi-wavelet dictionaries

In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.

Generator Unit Fault Diagnosis Using the Frequency Slice Wavelet Transform Time-frequency Analysis Method

为了提取有效的故障特征，提出了基于频率切片小波变换时频分解的故障特征分离提取方法。先对信号进行频率切片小波变换获取其时频分布，然后根据信号的能量分布特点选择时频区域，再以较高的时频分辨率对选择的时频区域进一步细化分析，以突出隐含在信号中的时频特征，在此基础上分割出含有故障特征时频区域，再通过滤波和逆变换重构分离出有效的故障特征。仿真实验和工程应用表明，这种方法可从噪声信号中分离出有效的特征分量，在发电机组故障特征提取时取得了较好的效果。

High-precision frequency attenuation analysis and its application

Based on seismic attenuation theory in a fluid-filled porous medium, we improve conventional methods of low-frequency shadow analysis （LFSA） and energy absorption analysis （EAA） and propose a high-precision frequency attenuation analysis technology. First, we introduce the method of three-parameter wavelet transform and the time-frequency focused criterion and develop a high-precision time-frequency analysis method based on an adaptive three-parameter wavelet transform, which has high time-frequency resolution with benefit to LFSA and can obtain a single-peaked spectrum with narrow side-lobes with benefit to EAA. Second, we correctly compute absorption coefficient by curve fitting based on the nonlinear Nelder-Mead algorithm and effectively improve EAA precision. Practical application results show that the proposed frequency attenuation analysis technology integrated with LFSA and EAA can effectively predict favorable zones of carbonate oolitic reservoir. Furthermore, reservoir prediction results based on LFSA correspond with EAA. The new technology can effectively improve reservoir prediction reliability and reduce exploration risk.

Improvement of the Mirror Extending in Empirical Mode Decomposition Method and the Technology for Eliminating Frequency Mixing

The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end of data is obviously not extremum, the envelope is determined by the first inner extremum and the image value in the mirror, ignoring the value on the end. This improvement eliminates the frequency compression near the end and decreases the error. Meanwhile, tridiagonal equations are used and the calculation speed is much increased. The temporal process curve is more important in reflecting the real physical process and comparable with other phenomena. Frequency mixing in IMFs makes it impossible. A high frequency reconstruction (HFR) approach is proposed to eliminate common frequency mixing and reconstruct an IMF with all high frequency portions. By this approach, the IMFs without frequency mixing are obtained to express significative processes. The high frequency information restored in high frequency IMF can be extracted by general spectrum method. After obtaining IMFs by EMD method, some of the theoretical and technological issues still exist when using the IMFs. The consistency of IMFs with real physical process is discussed in detail. By virtue of the approach proposed in this paper, the EMD method can be widely used in various fields.

Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Frequency domain based super-resolution method for mixed-resolution multi-view images

Super-resolution (SR) techniques, which are based on single or multi-frame low-resolution (LR) images, have been extensively investigated in the last two decades. Mixed-resolution multiview video format plays an important role in three-dimensional television (3DTV) coding scheme. Previous work considers multiview or multi-camera images and videos at the same resolution, which performs well under the planar model without or with little projection error among the videos captured by different cameras. In recent years, several researchers have discussed the SR problem in mixed-resolution multi-view video format, where the superresolved image is created using the up-sampled version of the LR image and the high frequency components extracted from the warped image in the adjacent high-resolution (HR) views. Unfortunately, the output HR images suffer from artifacts caused by depth error. To obtain the detailed texture and edge information from the HR image as much as possible, while preserving the structure of the LR image, a novel SR reconstruction algorithm is proposed. The algorithm is composed of three components: the structure term, the detail information term, and the regularization term. The first term preserves the structure similarity of the LR image; the second term extracts detailed information from the adjacent HR image; and the last term ensures the uniqueness of the solution. Experimental results show the effectiveness and robustness of the proposed algorithm, which achieves high performance both subjectively and objectively. © 2016 Beijing Institute of Aerospace Information.

High-resolution Image Reconstruction by Neural Network and Its Application in Infrared Imaging

As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.

FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

多尺度残差密集注意力网络图像超分辨率重建

目的使用单一尺度卷积网络提取低分辨率(low-resolution,LR)图像特征会造成大量图像高频特征丢失,为了获取更多高频特征,重建更清晰的超分辨率图像,方法提出一种基于多尺度残差密集注意力网络(multi-scale residual dense attention network)的单幅图像超分辨率重建算法。首先,使用卷积网络从低分辨率图像中提取浅层特征并将其作为后续网络各级输入;其次,采用各级多尺度残差密集注意力块(multi-scale residual dense attention block)处理前级网络图像特征并从中提取图像高频特征,多尺度残差密集网络善于提取更丰富的图像特征,并融入注意力机制,增强网络对高频区域特征的关注;然后,将网络各级提取不同深度的图像特征进行全局特征融合;最后,融合后的特征经上采样输出重建的超分辨率图像。结果放大因子为4时,网络在SET5,SET14,BSDS100,URBAN100和MANGA109数据集上测试,峰值信噪比分别为31.97,28.58,27.57,25.85,29.79 dB;网络中基本模块分别由多尺度残差密集注意力块、残差块和密集块替换提取特征,以峰值信噪比作为模块性能评估标准,多尺度残差密集注意力块表现更优异。结论该网络结合多尺度残差密集网络能够获取更丰富图像高低频信息,融入注意力机制有效对网络中高频信息进行提取,能重建纹理更清晰的超分辨率图像。

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。

基于多项式Chirplet变换的线性调频引信干扰波形设计方法

针对现代战场环境下线性调频引信干扰技术的迫切需求,提出多项式Chirplet变换的方法实现低信噪比下对调频连续波无线电引信的信号参数估计,并针对调频连续波引信重构了干扰信号。基于线性小波变换原理,选取适当的核函数,将无线电引信信号经过旋转与平移操作后进行短时傅里叶变换,再利用遗传算法求得变换核的最优参数,实现对调频连续波信号的参数估计,并重构干扰信号。仿真对比结果表明,该方法可以在较低信噪比下实现对调频连续波无线电引信信号载波频率、调制周期、最大频偏等参数进行精准估计,相较于周期调制类干扰,重构干扰信号能够以更低功率对调频引信产生较好的干扰效果。

基于多频特征和纹理增强的轻量化图像超分辨率重建

现有基于卷积神经网络主要关注图像重构的精度,忽略了过度参数化、特征提取不充分以及计算资源浪费等问题。针对上述问题,提出了一种轻量级多频率特征提取网络(MFEN),设计了轻量化晶格信息交互结构,利用通道分割和多模式卷积组合减少参数量;通过分离图像的低频、中频以及高频率信息后进行特征异构提取,提高网络的表达能力和特征区分性,使其更注重纹理细节特征的复原,并合理分配计算资源。此外,在网络内部融合局部二值模式(LBP)算法用于增强网络对纹理感知的敏感度,旨在进一步提高网络对细节的提取能力。经验证,该方法在复杂度和性能之间取得了良好的权衡,即实现轻量有效提取图像特征的同时重建出高分辨率图像。在Set5数据集上的2倍放大实验结果最终表明,相比较于基于卷积神经网络的图像超分辨率经典算法(SRCNN)和较新算法(MADNet),所提方法的峰值信噪比(PSNR)分别提升了1.31 dB和0.12 dB,参数量相比MADNet减少了55%。

赋能教育联合低频脉冲电治疗在神经源性膀胱功能重建中的应用效果研究

目的:探讨赋能教育联合低频脉冲电治疗在神经源性膀胱功能重建中的应用效果。方法:选择2022年1月-2024年3月在汕头大学医学院第二附属医院住院治疗的神经源性膀胱患者57例,其中,泌尿外科一病区患者为对照组,采用常规传统的护理方法;泌尿外科二病区患者为实验组,在对照组护理基础上采用赋能教育联合低频脉冲电治疗等康复护理,对比两组患者的膀胱功能康复效果。结果:实验组治疗后残余尿量相较于治疗前显著减少(P<0.05)。实验组与对照组相比,治疗效果有明显的统计学差异(P<0.05);实验组排尿量明显高于对照组(P<0.05)、实验组降低患者焦虑情绪效果更佳(P<0.05);常规传统的护理模式或者采用赋能教育的新型科学的护理康复措施,均有利于患者的尿流率改善。结论:赋能教育联合低频脉冲电治疗可有效促进神经源性膀胱患者功能重建,改善排尿障碍,减轻焦虑情绪,提高生活质量。

混合级联十一电平逆变器功率与开关损耗双均衡倍频调制方法

混合级联多电平逆变器可以利用较少的开关器件实现较高电平输出,进而得到了广泛应用;但实现高低压单元间功率均衡难度更大。直流侧采用电池组模块进行供电时,功率达不到均衡会使得各H桥单元对应的电池组模块处于不同的放电特性,增大了故障概率和维修难度且增加了后期的维修成本。针对此问题,提出2种1:1:1:2混合级联十一电平逆变器功率和开关损耗双均衡倍频调制方法。首先,2种方法分别对3个低压单元采用三角载波、重构载波进行移相,从而实现在全调制度范围内将逆变器的等效开关频率由fc提高至6fc/12fc;使得低压单元各开关器件开关次数均围绕在60次/174次左右上下浮动,实现了低压单元内开关损耗均衡;使得调制度M=0.9/0.6/0.3时低压单元输出功率均围绕在657.5 W/293.5 W/73.5 W上下浮动,实现低压单元间的功率均衡,其次对高压单元采用阶梯波低频调制,随调制度的变化对导通角进行动态调整,使得高压单元各开关器件开关次数均为1次,实现了高低压单元内开关损耗均衡;使得调制度M=0.9/0.6/0.3时高压单元与低压单元输出功率均围绕在1317 W/657.5 W、586 W/293.5 W、146.5 W/73.5 W上下浮动,实现了高低压单元间功率均衡,之后对比分析2种方法各自的优缺点及其适用场合。最后,通过实验验证该方法的正确性和可行性,并给出了直流电压比为1:1:1:...:x的混合级联H桥逆变器双均衡的普适方法。

基于高低频特征增强算法的室内场景三维重建仿真

为了提高室内设计效率和质量,提出了一种基于高低频特征增强算法的室内场景三维重建仿真方法。利用小波变换算法提取室内场景单图像高低频特征,通过加权引导滤波算法增强高低频特征图像;采用深度神经网络,预测图像的分割图和深度图,并结合长短时记忆网络估计室内场景单图像平面参数。然后整合分割图、深度图与平面参数,实现三维重建。实验结果表明:该方法可有效提取室内场景单图像高低频特征,增强高低频特征图像和提升图像清晰度;三维重建均方归一化误差最高值为0.15。

结合高频感知的大气偏振模式生成方法

大气偏振模式是一种稳定的自然属性,其在导航、探测等领域有广泛的应用,但由于自然环境以及周边建筑物遮挡的影响,在同一时刻获取的大气偏振信息是局部且不连续的,导致其在实际应用中受到影响。现有方法主要对大气偏振模式进行大范围图像的修复,对于高频信号的修复精度十分有限导致边缘模糊。针对该问题,本文采用软分割软合成的方法,对偏振信息进行冗余分割并合成,避免了高频信号的丢失,挖掘每个局部中的高频信号特征,并根据大气偏振模式时空连续性合理推测,保证重构的信息与真实信息维持一致,从而生成完整连续的大气偏振信息。实验结果证明,本方法能够很好地重构大气偏振模式中缺失的偏振信息,在云层干扰大于40%的实测重构实验中,本文方法的SSIM和PSNR得分相较于其他方法提高了26%和12%。

一种卫星隐蔽通信信号盲分离算法

重构抵消算法是卫星隐蔽通信信号分离的关键技术,算法的性能主要依赖于参数估计的精度。然而在实际环境中,参数估计误差带来的算法性能损失无法被避免。此外,对信号的重构使得该算法计算复杂度较高。针对这个问题,首先分析了参数估计误差对分离性能的影响,然后提出基于盲均衡算法的协作分离算法,提升信号分离性能的同时降低了算法的计算量。仿真实验表明,新算法相较于重构抵消算法,降低了对参数估计精度的依赖,当参数估计误差大于0.05时,信号的解调误码率降低了一个数量级左右。