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Gravitational wave search by time-scale-recursive denoising and matched filtering

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Science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 166-182页

作者： Cunliang Ma Chenyang Ma Zhoujian Cao Mingzhen Jia School of Information Engineering Jiangxi University of Science and TechnologyGanzhou341000China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing102206China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCASHangzhou310024China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000China

In our previous work [Physical Review D,2024,109(4):043009],we introduced MSNRnet,a framework integrating deep learning and matched filtering methods for gravitational wave(GW) *** with end-to-end classification methods,MSNRnet is physically *** denoising models and astrophysical discrimination models corresponding to different parameter space were operated independently for the template prediction and *** the MSNRnet has a lot of computational *** this study,we propose a new framework for template prediction,which significantly improves our previous *** new framework consists of the recursive application of denoising models and waveform classification models,which solve the problem of computational *** waveform classification network categorizes the denoised output based on the signal's time *** enhance the denoising performance for long-time-scale data,we upgrade the denoising model by incorporating Transformer and ResNet ***,we introduce a novel training approach that allows for the simultaneous training of the denoising network and waveform classification network,eliminating the need for manual annotation of the waveform dataset required in our previous ***-data analysis results demonstrate that our new method decreases the false alarm rate by approximately 25%,boosts the detection rate by roughly 5%,and slashes the computational cost by around 90%.The new method holds potential for future application in online GW data processing.

关键词： gravitational wave matched filtering deep learning

An extended processing scheme for coherent integration and parameter estimation based on matched filtering in passive radar

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第11期15卷 1071-1085页

作者： Xin GUAN Li-hua ZHONG Dong-hui HU Chi-biao DING Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

关键词： Keystone transform matched filtering MDCFT Mismatching Passive radar Velocity estimation

Automatic Piano Music Transcription Using Audio-Visual Features

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Chinese Journal of Electronics 2015年第3期24卷 596-603页

作者： WAN Yulong WANG Xianliang ZHOU Ruohua YAN Yonghong The Key Laboratory of Speech Acoustics and Content Understanding Chinese Academy of Sciences

The performance of automatic music transcription seems to have reached a limit over the last decade,and a promising direction of improvements could be to incorporate music instruments' specific parameters. We propose a novel piano-specific transcription system, using both audio and visual features for the first time. Contribution of the paper mainly includes two parts: A new onset detection method is proposed using a specific spectrum envelope matched filter on multiple frequency bands. A computer-vision method is proposed to enhance audio-only piano music transcription, through tracking the positions of the pianist's hands on the piano keyboard. Based on the MIDI Aligned piano sounds(MAPS) database and a selfrecorded video database, we carried out comparable experiments for audio-only onset detection and overall system,respectively. The performance was compared with the best piano transcription system in Music information retrieval evaluation exchange(MIREX), and the results showed that the proposed system outperforms the state-of-art method substantially.

关键词： Music transcription Polyphonic piano Onset detection matched filtering Multimedia fusion

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

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Petroleum Science 2023年第4期20卷 2127-2140页

作者： Nan-Ying Lan Fan-Chang Zhang Kai-Heng Sang Xing-Yao Yin School of Geosciences China University of Petroleum(East China)QingdaoShandong266580China

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

关键词： Simultaneous denoising and resolution enhancement Elastic convolution dictionary learning Weighted convolutional sparse representation matched filtering

Application of Newtonian approximate model to LIGO gravitational wave data processing

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Chinese Physics B 2023年第9期32卷 192-199页

作者：吴洁李瑾蒋青权 College of Physics Chongqing UniversityChongqing 401331China Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics Chongqing UniversityChongqing 401331China School of Physics and Astronomy China West Normal UniversityNanchong 637009China

With the observation of a series of ground-based laser interferometer gravitational wave(GW)detectors such as LIGO and Virgo,nearly 100 GW events have been detected *** present,all detected GW events are generated by the mergers of compact binary systems and are identified through the data processing of matched *** on matched filtering,we use the GW waveform of the Newtonian approximate(NA)model constructed by linearized theory to match the events detected by LIGO and injections to determine the coalescence time and utilize the frequency curve for data fitting to estimate the parameters of the chirp masses of binary black holes(BBHs).The average chirp mass of our results is 22.05_(-6.31)^(+6.31)M_(⊙),which is very close to 23.80_(-3.52)^(+4.83)M_(⊙)provided by *** the process,we can analyze LIGO GW events and estimate the chirp masses of the *** work presents the feasibility and accuracy of the low-order approximate model and data fitting in the application of GW data *** is beneficial for further data processing and has certain research value for the preliminary application of GW data.

关键词： gravitational waves black holes matched filtering data fitting

Suppression of dense false target jamming for stepped frequency radar in slow time domain

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Science China(Information Sciences) 2022年第3期65卷 283-285页

作者： Li ZENG Zhaojian ZHANG Yong-Liang WANG Weijian LIU Hui CHEN Bilei ZHOU Electronic Information School Wuhan University Wuhan Electronic Information Institute

Dear editor,The presence of jamming can drastically degrade the detection performance of a radar system [1]. Dense false target jamming is a paramount type of deceptive jamming, which is employed to counter pulse compression radar [2]. The jamming signal is obtained by storing and transmitting the radiated signal, and it produces a large number of false targets that are similar to target signals at the radar receiver [3].

关键词： stepped frequency dense false target jamming coherent accumulation slow time domain matched filtering

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Sparse SAR imaging based on L1/2 regularization

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Science China(Information Sciences) 2012年第8期55卷 1755-1775页

作者： ZENG JinShan,FANG Jian&XU ZongBen Institute for Information and System Sciences&Ministry of Education Key Lab for Intelligent Networks and Network Security,Xi’an Jiaotong University,Xi’an 710049,China 1. Institute for Information and System Sciences & Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an 710049 China

In this paper,a novel method for synthetic aperture radar(SAR)imaging is *** approach is based on L1/2 regularization to reconstruct the scattering field,which optimizes a quadratic error term of the SAR observation process subject to the interested scene *** to the conventional SAR imaging technique,the new method implements SAR imaging effectively at much lower sampling rate than the Nyquist rate,and produces high-quality images with reduced sidelobes and increased ***,over the prevalent greedy pursuit and L1 regularization based SAR imaging methods,there are remarkable performance improvements of the new *** one hand,the new method significantly reduces the number of measurements needed for reconstruction,as supported by a phase transition diagram *** the other hand,the new method is more robust to the observation *** fundamental properties of the new method are supported and demonstrated both by simulations and real SAR data experiments.

关键词： synthetic aperture radar matched filtering compressed sensing L1/2 regularization L1 regularization

Data Analysis Methods and Signal Processing Techniques in Gravitational Wave Detection

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Journal of Applied Mathematics and Physics 2024年第11期12卷 3774-3783页

作者： Bojun Yan Linyi NO.1 Middle School of Shandong Province Linyi China

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides a comprehensive review of data analysis methods and signal processing techniques in gravitational wave detection. The research begins by introducing the characteristics of gravitational wave signals and the challenges faced in their detection, such as extremely low signal-to-noise ratios and complex noise backgrounds. It then systematically analyzes the application of time-frequency analysis methods in extracting transient gravitational wave signals, including wavelet transforms and Hilbert-Huang transforms. The study focuses on discussing the crucial role of matched filtering techniques in improving signal detection sensitivity and explores strategies for template bank optimization. Additionally, the research evaluates the potential of machine learning algorithms, especially deep learning networks, in rapidly identifying and classifying gravitational wave events. The study also analyzes the application of Bayesian inference methods in parameter estimation and model selection, as well as their advantages in handling uncertainties. However, the research also points out the challenges faced by current technologies, such as dealing with non-Gaussian noise and improving computational efficiency. To address these issues, the study proposes a hybrid analysis framework combining physical models and data-driven methods. Finally, the research looks ahead to the potential applications of quantum computing in future gravitational wave data analysis. This study provides a comprehensive theoretical foundation for the optimization and innovation of gravitational wave data analysis methods, contributing to the advancement of gravitational wave astronomy.

关键词： Gravitational Wave Detection Data Analysis Signal Processing matched filtering Machine Learning

Radar Imaging of Sidelobe Suppression Based on Sparse Regularization

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Journal of Computer and Communications 2016年第3期4卷 108-115页

作者： Xiaoxiang Zhu Guanghu Jin Feng He Zhen Dong Institute of Space Electronic and Information Technology School of Electronic Science and Engineering National University of Science and Technology Changsha China

Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.

关键词： matched filtering Sparse Representation Sparse Reconstruction Convex Optimization Greed Algorithm