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.

TURBO AIDED CYCLIC PREFIX RECONSTRUCTION FOR CODED SINGLE-CARRIER SYSTEMS WITH FREQUENCY-DOMAIN EQUALIZATION (SC-FDE)

In this paper, a Turbo aided Cyclic Prefix (CP) reconstruction scheme, termed Turbo- CPR, is proposed for Single-Carrier systems with Frequency-Domain Equalization (SC-FDE) that employ insufficient CP in the transmitter. In Turbo-CPR, the decoder output is incorporated in the process of equalization, i.e. Turbo equalizer is employed. It is shown in the simulation results that Turbo-CPR not only recovers the performance loss due to insufficiency of CP, but also provides extra gains over the lower bound of performance for conventional CP reconstruction schemes.

EFFICIENT CYCLIC PREFIX RECONSTRUCTION USING DECISION FEEDBACK FILTER FOR CODED SINGLE-CARRIER SYSTEMS WITH FREQUENCY-DOMAIN EQUALIZATION (SC-FDE)

In this paper,an efficient Cyclic Prefix (CP) reconstruction scheme is proposed for Sin-gle-Carrier systems with Frequency-Domain Equalization (SC-FDE) that employ insufficient length of CP at the transmitter. By utilizing a decision feedback filter to cancel the residual InterSymbol In-terference (ISI) in the equalized signal,the proposed scheme can effectively lower the low bound of performance for the CP reconstruction schemes and can greatly improve the Bit Error Rate (BER) performance of SC-FDE systems. In addition,the existing methods and the proposed scheme are also optimized. It is shown in the simulation results that,when the Signal-to-Noise Ratio (SNR) exceeds a certain threshold,the proposed scheme can achieve the low bound of performance for the existing methods. Moreover,by increasing the number of iteration or through optimization,the low bound can be outperformed.

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.

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。

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.

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.

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%。

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ILVD的多分量LFM信号重构算法

针对线性调频(linear frequency modulated, LFM)信号参数估计技术面向多目标产生的多分量信号会产生交叉项,从而降低参数估计精度的问题,提出了基于逆吕氏分布(inverse Lv’s distribution, ILVD)的多分量LFM信号重构算法。利用特征值分解结合最小误差准则方法的中心频率-调频斜率(centroid frequency-chirp rate, CFCR)域信号分析与合成,克服传统时频变换方法中的交叉项干扰问题;采用特征值分解思想构造ILVD变换核函数;使用最小误差准则校正CFCR域随机相位误差,重构出分布在CFCR域吕氏分布中不同位置对应的各个目标信号。实验结果表明,提出的算法在重构信号的准确度和抗噪声干扰方面的有效性均有较大提高。

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%。

巨厚高原山地式页岩高频沉积旋回特征与有机质富集机理

高频沉积旋回是地球轨道驱动力周期性变化引起海平面升降和气候波动的高频周期性响应(吴怀春等,2011)。同时,高频沉积旋回对有机质富集和储集空间的分布具有重要的控制作用(钟寿康等,2022)。因此,高频沉积旋回分析不仅有利于揭示古气候演化、古环境重建和古地貌恢复等重要的基础科学问题,而且能为油气实践勘探提供重要理论指导。柴达木盆地位于青藏高原东北部.

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。

Whispering-gallery-mode barcode-based broadband sub-femtometer-resolution spectroscopy with an electro-optic frequency comb

Spectroscopy is the basic tool for studying molecular physics and realizing biochemical sensing.However,it is challenging to realize sub-femtometer resolution spectroscopy over broad bandwidth.Broadband and high-resolution spectroscopy with calibrated optical frequency is demonstrated by bridging the fields of speckle pattern and electro-optic frequency comb.A wavemeter based on a whispering-gallery-mode barcode is proposed to link the frequencies of a probe continuous-wave laser and an ultrastable laser.The ultrafine electro-optic comb lines are generated from the probe laser to record spectrum of sample with sub-femtometer resolution.Measurement bandwidth is a thousandfold broader than comb bandwidth,by sequentially tuning the probe laser while its wavelength is determined.This approach fully exploits the advantages of two fields to realize 0.8-fm resolution with a fiber laser and 80-nm bandwidth with an external cavity diode laser.The spectroscopic measurements of an ultrahigh Q-factor cavity and gas molecular absorption are experimentally demonstrated.The compact system,predominantly constituted by few-gigahertz electronics and telecommunication components,shows enormous potential for practical spectroscopic applications.

三参数W变换的可逆性

三参数W变换(TPWT)是分析非平稳信号的有效工具,已成功应用于油气储层识别。然而,前人没有详细论述TPWT的可逆性。为此,首先回顾了TPWT的基本原理,然后从理论上探讨了TPWT的可逆性。理论分析与数值计算结果表明:(1)TPWT既解决了S变换(ST)的低频时间分辨率低与时—频谱能量分布中心向高频偏移的问题,也克服了W变换(WT)时—频谱在主频处的振幅分裂现象,能更准确地描述油气储层,更有利于地震解释。(2)在理论上TPWT不是严格可逆的,而是一种近似可逆的变换工具,这与傅里叶变换(FT)、ST不同。(3)合成地震数据与实际地震记录的数值计算结果显示,与原始地震数据相比,利用逆TPWT重建地震数据的相对误差为11.47%~21.35%,即理论上的不可逆导致较大的重建误差,严重影响TPWT的应用范围,在去噪、高分辨率处理等需要重建数据的处理领域不宜使用。

不同域下基于UNet的三维地震数据重建

由于自然因素的限制以及经济条件的约束,采集到的地震数据通常存在地震道的缺失,需对缺失数据进行插值。然而,目前基于深度学习的方法大多在某一固定的域下完成地震数据的插值。为了对比不同域下地震数据重建结果的差异,本文基于UNet网络,对比了五种不同域下的地震数据插值方法,分别为:t-x(time-space)域,时间切片,f-x(frequency-space)域,频率域按炮切片以及三维方法。在仿真数据集和真实数据集上进行了实验,通过三维重建结果,二维切片,不同缺失率的地震数据重建信噪比以及五种方法的耗时对比,全面展示了几种方法在地震数据重建问题上的差异。实验结果显示,五种方法在一定程度上均可重建缺失数据,其中三维方法可以取得最高的重建精度,和基于f-x域的方法相比,重建信噪比提高了近2 dB;但三维方法以三维数据块作为输入训练网络,所以最为耗时,网络一轮的训练时间比其它方法多70~170 min。此外,对不同缺失率的地震数据重建结果显示,三维方法和基于f-x域的方法在重建高缺失率的地震数据时更有优势。