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sparse recovery of Linear Time-Varying Channel in OFDM System

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Journal of Beijing Institute of Technology 2017年第2期26卷 245-251页

作者： Jiansheng Hu Zuxun Song Shuxia Guo School of Electronics and Information Northwestern Polytechnical University Xi'an 710072China

In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.

关键词： orthogonal frequency division multiplexing （ OFDM ） linear time-varying （LTV） channel sparse recovery pilots design

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sparse Signal recovery via Exponential Metric Approximation

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Tsinghua Science and Technology 2017年第1期22卷 104-111页

作者： Jian Pan Jun Tang Wei Zhu Department of Electronic Engineering Tsinghua University Beijing 100084China

sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint l_0-quasinorm minimization problem, which is thought to be a Non-deterministic Polynomial-time（NP）-hard problem. Although several approximation methods have been developed to solve this problem via convex relaxation, researchers find the nonconvex methods to be more efficient in solving sparse recovery problems than convex methods. In this paper a nonconvex Exponential Metric Approximation（EMA）method is proposed to solve the sparse signal recovery problem. Our proposed EMA method aims to minimize a nonconvex negative exponential metric function to attain the sparse approximation and, with proper transformation,solve the problem via Difference Convex（DC） programming. Numerical simulations show that exponential metric function approximation yields better sparse recovery performance than other methods, and our proposed EMA-DC method is an efficient way to recover the sparse signals that are buried in noise.

关键词： sparse recovery exponential metric approximation sparsity tolerance DC optimization signal-to-noiseratio

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DOA estimation method for wideband signals by sparse recovery in frequency domain

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Journal of Systems Engineering and Electronics 2017年第5期28卷 871-878页

作者： Jiaqi Zhen Zhifang Wang College of Electronic Engineering Heilongjiang University

The traditional super-resolution direction finding methods based on sparse recovery need to divide the estimation space into several discrete angle grids, which will bring the final result some error. To this problem, a novel method for wideband signals by sparse recovery in the frequency domain is proposed. The optimization functions are found and solved by the received data at every frequency, on this basis, the sparse support set is obtained, then the direction of arrival (DOA) is acquired by integrating the information of all frequency bins, and the initial signal can also be recovered. This method avoids the error caused by sparse recovery methods based on grid division, and the degree of freedom is also expanded by array transformation, especially it has a preferable performance under the circumstances of a small number of snapshots and a low signal to noise ratio (SNR).

关键词： super-resolution direction of arrival sparse recovery frequency domain wideband signals

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Robust and fast iterative sparse recovery method for space-time adaptive processing

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Science China(Information Sciences) 2016年第6期59卷 199-211页

作者： Xiaopeng YANG Yuze SUN Tao ZENG Teng LONG Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology

Conventional space-time adaptive processing(STAP) requires large numbers of independent and identically distributed(i.i.d) training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.

关键词： space-time adaptive processing(STAP) sparse recovery robust iteration computational complexity

PIECEWISE sparse recovery VIA PIECEWISE INVERSE SCALE SPACE ALGORITHM WITH DELETION RULE

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Journal of Computational Mathematics 2020年第2期38卷 375-394页

作者： Yijun Zhong Chongjun Li School of Mathematical Sciences Dalian University of TechnologyDalian 116024China

In some applications,there are signals with piecewise structure to be recovered.In this paper,we propose a piecewise_ISS(P_ISS)method which aims to preserve the piecewise sparse structure(or the small-scaled entries)of piecewise signals.In order to avoid selecting redundant false small-scaled elements,we also implement the piecewise_ISS algorithm in parallel and distributed manners equipped with a deletion rule.Numerical experiments indicate that compared with alSS,the P_ISS algorithm is more effective and robust for piecewise sparse recovery.

关键词： Inverse scale space Piecewise sparse sparse recovery Small-scaled entries

Denoising enabled channel estimation for underwater acoustic communications:A sparsity-aware model-driven learning approach

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Intelligent and Converged Networks 2023年第1期4卷 1-14页

作者： Sicong Liu Younan Mou Xianyao Wang Danping Su Ling Cheng School of Informatics Xiamen UniversityXiamen 361005China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the Information Engineering University of the WitwatersrandJohannesburg 2050South Africa

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions,including frequency-selective property,high relative mobility,long propagation latency,and intensive ambient noise,etc.To this end,a deep unfolding neural network based approach is proposed,in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning,and a scheme based on the Sparsity-Aware DNN(SA-DNN)for UAC estimation is proposed to improve the estimation accuracy.Moreover,we propose a Denoising Sparsity-Aware DNN(DeSA-DNN)based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network,so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved.Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision,pilot overhead,and robustness,particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots.

关键词： Orthogonal Frequency Division Multiplexing(OFDM) Underwater Acoustic Communications(UAC) sparse recovery deep learning sparse learning denoising approximate message passing

Joint Bayesian and Greedy recovery for Compressive Sensing

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Chinese Journal of Electronics 2020年第5期29卷 945-951页

作者： LI Jia China Electronics Technology Group Corporation 54th Research Institute Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control

Greedy algorithms are widely used for sparse recovery in compressive sensing. Conventional greedy algorithms employ the inner product vector of signal residual and sensing matrix to determine the support, which is based on the assumption that the indexes of the larger-magnitude entries of the inner product vector are more likely to be contained in the correct supports.However, this assumption may be not valid when the number of measurements is not sufficient, leading to the selection of an incorrect support. To improve the accuracy of greedy recovery, we propose a novel greedy algorithm to recover sparse signals from incomplete measurements.The entries of a sparse signal are modelled by the type-II Laplacian prior, such that the k indexes of the correct support are indicated by the largest k variance hyperparameters of the entries. Based on the proposed model, the supports can be recovered by approximately estimating the hyperparameters via the maximum a posteriori process. Simulation results demonstrate that the proposed algorithm outperforms the conventional greedy algorithms in terms of recovery accuracy, and it exhibits satisfactory recovery speed.

关键词： sparse recovery Compressive sensing Greedy algorithm Laplacian prior

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

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Science China(Information Sciences) 2016年第6期59卷 225-239页

作者： Qian BAO Kuoye HAN Xueming PENG Wen HONG Bingchen ZHANG Weixian TAN Science and Technology on Microwave Imaging Laboratory (MITL) Institute of ElectronicsChinese Academy of Sciences (IECAS) School of Electronic and Communication Engineering University of Chinese Academy of Sciences (UCAS) Information System Technology Institute Information Science Academy of China Electronics Technology Group Corporation Global RD Centre (Shanghai) Carestream Health Incorporated College of Information Engineering Inner Mongolia University of Technology

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar(DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However,most of the existing compressive sensing(CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization(ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm(pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming(SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.

关键词： atomic norm minimization DLSLA 3-D SAR sparse recovery off-grid targets pseudo-PFA 3-D imaging

DOA Estimation Using Block Variational sparse Bayesian Learning

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Chinese Journal of Electronics 2017年第4期26卷 768-772页

作者： HUANG Qinghua ZHANG Guangfei FANG Yong Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University

In Direction-of-arrival(DOA) estimation,the real-valued sparse Bayesian algorithm degrades the estimation performance by decomposing the complex value into real and imaginary components and combining them independently. We directly use complex probability density functions to model the noise and complex-valued sparse direction weights. Based on the Multiple measurement vectors(MMV), block sparse structure for the direction weights is integrated into the variational Bayesian learning to provide accurate source direction estimates. The proposed algorithm can be used for arbitrary array geometries and does not need the prior information of the incident signal number. Simulation results demonstrate the better performance of the proposed method compared with the real-valued sparse Bayesian algorithm, the Orthogonal matching pursuit(OMP) and l1 norm based complexvalued methods.

关键词： Direction-of-arrival estimation Varia tional Bayesian learning sparse recovery Multiple mea surement vectors Block sparse structure