A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conven...A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.展开更多
With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direc...With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.展开更多
Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or ...Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.展开更多
With a low resolution 1-bit ADC on its receiver(RX) side, MIMO with 1-bit ADC took a considerable step in the fulfillment of the hardware complexity constrains of the internet of things(IoT) PHY layer design. However,...With a low resolution 1-bit ADC on its receiver(RX) side, MIMO with 1-bit ADC took a considerable step in the fulfillment of the hardware complexity constrains of the internet of things(IoT) PHY layer design. However, applying 1-bit ADC at MIMO RX results in severe nonlinear quantization error. By which, almost all received signal amplitude information is completely distorted. Thus, MIMO channel estimation is considered as a major barrier towards practical realization of 1-bit ADC MIMO system. In this paper, two efficient sparsity-based channel estimation techniques are proposed for 1-bit ADC MIMO systems, namely the low complexity sparsity-based channel estimation(LCSCE), and the iterative adaptive sparsity channel estimation(IASCE). In these techniques, the sparsity of the 1-bit ADC MIMO channel is exploited to propose a new adaptive and iterative compressive sensing(CS) recovery algorithm to handle the 1-bit ADC quantization effect. The proposed algorithms are tested with the state-of-the-art 1-bit ADC MIMO constant envelope modulation(MIMO-CEM). The 1-bit ADC MIMO-CEM system is chosen as it fulfills both energy and hardware complexity constraints of the IoT PHY layer. Simulation results reveal the high effectiveness of the proposed algorithms in terms of spectral efficiency(SE) and computational complexity. The proposed LCSCE reduces the computational complexity of the 1-bit ADC MIMO-CEM channel estimation by 86%, while the IASCE reduces it by 96% compared to the recent techniques of MIMO-CEM channel estimation. Moreover, the proposed LCSCE and IASCE improve the spectrum efficiency by 76 % and 73 %, respectively, compared to the recent techniques.展开更多
Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for ...Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.展开更多
This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MR...This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.展开更多
文摘A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.
基金supported by the National Basic Research Program of China。
文摘With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
基金supported by the Engineering and Physical Sciences Research Council of UK (Grant No. #EP/K00946X/1)
文摘Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.
文摘With a low resolution 1-bit ADC on its receiver(RX) side, MIMO with 1-bit ADC took a considerable step in the fulfillment of the hardware complexity constrains of the internet of things(IoT) PHY layer design. However, applying 1-bit ADC at MIMO RX results in severe nonlinear quantization error. By which, almost all received signal amplitude information is completely distorted. Thus, MIMO channel estimation is considered as a major barrier towards practical realization of 1-bit ADC MIMO system. In this paper, two efficient sparsity-based channel estimation techniques are proposed for 1-bit ADC MIMO systems, namely the low complexity sparsity-based channel estimation(LCSCE), and the iterative adaptive sparsity channel estimation(IASCE). In these techniques, the sparsity of the 1-bit ADC MIMO channel is exploited to propose a new adaptive and iterative compressive sensing(CS) recovery algorithm to handle the 1-bit ADC quantization effect. The proposed algorithms are tested with the state-of-the-art 1-bit ADC MIMO constant envelope modulation(MIMO-CEM). The 1-bit ADC MIMO-CEM system is chosen as it fulfills both energy and hardware complexity constraints of the IoT PHY layer. Simulation results reveal the high effectiveness of the proposed algorithms in terms of spectral efficiency(SE) and computational complexity. The proposed LCSCE reduces the computational complexity of the 1-bit ADC MIMO-CEM channel estimation by 86%, while the IASCE reduces it by 96% compared to the recent techniques of MIMO-CEM channel estimation. Moreover, the proposed LCSCE and IASCE improve the spectrum efficiency by 76 % and 73 %, respectively, compared to the recent techniques.
基金This work was supported by the Fundamental Research Funds for the Central Universities(NE2020004)the National Natural Science Foundation of China(61901213)+3 种基金the Natural Science Foundation of Jiangsu Province(BK20190397)the Aeronautical Science Foundation of China(201920052001)the Young Science and Technology Talent Support Project of Jiangsu Science and Technology Associationthe Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(kfjj20200419).
文摘Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.
基金Natural Science Foundation of Chinagrant number:81371635+3 种基金Research Fund for the Doctoral Program of Higher Education of Chinagrant number:20120131110062Shandong Province Science and Technology Development Plangrant number:2013GGX10104
文摘This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.
