With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the ac...With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.展开更多
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.展开更多
This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy model...This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.展开更多
Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed s...Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.展开更多
The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers ...The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing (DCS) is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS's main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.展开更多
Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel E...Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.展开更多
基金the financial support of this work by the National Natural Science Foundation of Hebei Province China under Grant E2020208052.
文摘With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output ( MIMO ) orthogonal frequency division multiplexing ( OFDM ) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
基金This work has been supported by the National Key R&D Program of China(Grant No.2018YFE0207600)EPSRC Research Grant(EP/K033700/1,EP/K033166/1)+2 种基金the Natural Science Foundation of China(61671046,61911530216,U1834210)the Beijing Natural Science Foundation(4182050)the FWO(Grants G0A2617N and G093817N).
文摘This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.
基金Supported by the National Natural Science Foundation of China (No. 60971129)the National Research Program of China (973 Program) (No. 2011CB302303)the Scientific Innovation Research Program of College Graduate in Jiangsu Province (No. CXLX11_0408)
文摘Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant Nos. 61203321 and 61374135), China Postdoctoral Science Foundation (2012M521676), China Central Universities Foundation (106112013CDJZR170005) and Postdoctoral scientific research project of Chongqing special funding (Xm201307).
文摘The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing (DCS) is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS's main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.
基金supported by National Natural Science Foundation of China under Grant Nos.61901409 and 61961013Jiangxi Provincial Natural Science Foundation under Grant No.20202BABL212001Open Project of State Key Laboratory of Marine Resources Utilization in South China Sea under Grant No.MRUKF2021034.
文摘Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.