scholarly journals Gridless Multiple Measurements Method for One-Bit DOA Estimation with a Nested Cross-Dipole Array

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haining Long ◽  
Ting Su ◽  
Xianpeng Wang ◽  
Mengxing Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Multiple Signal Classification ◽  
Multiple Measurement Vectors ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Alternating Direction ◽  
Multiple Measurements ◽  
The One ◽  
Better Than

The gridless one-bit direction of arrival (DOA) estimator is proposed to estimate electromagnetic (EM) sources on a nested cross-dipole array, and the multiple measurement vectors (MMV) mode is introduced to improve the reliability of parameter estimation. The gridless method is based on atomic norm minimization, solved by alternating direction multiplier method (ADMM). With gridless method used, sign inconsistency caused by one-bit measurements and basis mismatches by traditional grid-based algorithms can be avoided. Furthermore, the reconstructed denoising measurements with fast convergence and stable recovery accuracy are obtained by ADMM. Finally, spatial smoothing root multiple signal classification (SSRMUSIC) and dual polynomial (DP) methods are used, respectively, to estimate the DOAs on the reconstructed denoising measurements. Numerical results show that our method one-bit ADMM-SSRMUSIC has a better performance than that of one-bit SSRMUSIC used directly. At low signal to noise ratio (SNR) and low snapshot, the one-bit ADMM-DP has an excellent performance which is even better than that of unquantized MUSIC. In addition, the proposed methods are also suitable for both completely polarized (CP) signals and partially polarized (PP) signals.

scholarly journals Superresolution 2D DOA Estimation for a Rectangular Array via Reweighted Decoupled Atomic Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Ming-Ming Liu ◽  
Chun-Xi Dong ◽  
Yang-Yang Dong ◽  
Guo-Qing Zhao
Keyword(s):  
Computational Complexity ◽  
Optimization Problem ◽  
Toeplitz Matrices ◽  
Doa Estimation ◽  
Rank Minimization ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Surrogate Function

This paper proposes a superresolution two-dimensional (2D) direction of arrival (DOA) estimation algorithm for a rectangular array based on the optimization of the atomic l0 norm and a series of relaxation formulations. The atomic l0 norm of the array response describes the minimum number of sources, which is derived from the atomic norm minimization (ANM) problem. However, the resolution is restricted and high computational complexity is incurred by using ANM for 2D angle estimation. Although an improved algorithm named decoupled atomic norm minimization (DAM) has a reduced computational burden, the resolution is still relatively low in terms of angle estimation. To overcome these limitations, we propose the direct minimization of the atomic l0 norm, which is demonstrated to be equivalent to a decoupled rank optimization problem in the positive semidefinite (PSD) form. Our goal is to solve this rank minimization problem and recover two decoupled Toeplitz matrices in which the azimuth-elevation angles of interest are encoded. Since rank minimization is an NP-hard problem, a novel sparse surrogate function is further proposed to effectively approximate the two decoupled rank functions. Then, the new optimization problem obtained through the above relaxation can be implemented via the majorization-minimization (MM) method. The proposed algorithm offers greatly improved resolution while maintaining the same computational complexity as the DAM algorithm. Moreover, it is possible to use a single snapshot for angle estimation without prior information on the number of sources, and the algorithm is robust to noise due to its iterative nature. In addition, the proposed surrogate function can achieve local convergence faster than existing functions.

scholarly journals Grid-Free DOD and DOA Estimation for MIMO Radar via Duality-Based 2D Atomic Norm Minimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 60827-60836 ◽  
Author(s):  
Wen-Gen Tang ◽  
Hong Jiang ◽  
Shuai-Xuan Pang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Norm Minimization ◽  
Atomic Norm

scholarly journals DOA estimation based on sum–difference coarray with virtual array interpolation concept

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yarong Ding ◽  
Shiwei Ren ◽  
Weijiang Wang ◽  
Chengbo Xue
Keyword(s):  
Numerical Simulation ◽  
Degrees Of Freedom ◽  
Doa Estimation ◽  
Superior Performance ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Virtual Array ◽  
Simulation Results ◽  
The Difference ◽  
Coprime Array

AbstractThe sum–difference coarray is the union of difference coarray and the sum coarray, which is capable to obtain a higher number of degrees of freedom (DOF) than the difference coarray. However, this method fails to use all information provided by the coprime array because of the existence of holes. In this paper, we introduce the virtual array interpolation into the sum–difference coarray domain. After interpolating the virtual array, we estimate the DOA by reconstructing the covariance matrix to resolve an atomic norm minimization problem in a gridless way. The proposed method is gridless and can effectively utilize the DOF of a larger virtual array. Numerical simulation results verify the effectiveness and the superior performance of the proposed algorithm.

scholarly journals Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2222
Author(s):  
Jie Pan ◽  
Fu Jiang
Keyword(s):  
Array Signal Processing ◽  
Linear Array ◽  
Mimo Systems ◽  
Super Resolution ◽  
Doa Estimation ◽  
Superior Performance ◽  
Estimation Methods ◽  
Iteration Algorithm ◽  
Norm Minimization ◽  
Atomic Norm

Beamspace processing has become much attractive in recent radar and wireless communication applications, since the advantages of complexity reduction and of performance improvements in array signal processing. In this paper, we concentrate on the beamspace DOA estimation of linear array via atomic norm minimization (ANM). The existed generalized linear spectrum estimation based ANM approaches suffer from the high computational complexity for large scale array, since their complexity depends upon the number of sensors. To deal with this problem, we develop a low dimensional semidefinite programming (SDP) implementation of beamspace atomic norm minimization (BS-ANM) approach for DFT beamspace based on the super resolution theory on the semi-algebraic set. Then, a computational efficient iteration algorithm is proposed based on alternating direction method of multipliers (ADMM) approach. We develop the covariance based DOA estimation methods via BS-ANM and apply the BS-ANM based DOA estimation method to the channel estimation problem for massive MIMO systems. Simulation results demonstrate that the proposed methods exhibit the superior performance compared to the state-of-the-art counterparts.

Blind sparse-spike deconvolution with thin layers and structure

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. V481-V496
Author(s):  
Yuhan Sui ◽  
Jianwei Ma
Keyword(s):  
Matrix Factorization ◽  
Sparse Matrix ◽  
Thin Layers ◽  
Deconvolution Method ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Seismic Image ◽  
Convolution Model ◽  
Sparse Matrix Factorization ◽  
Better Than

Blind sparse-spike deconvolution is a widely used method to estimate seismic wavelets and sparse reflectivity in the shape of spikes based on the convolution model. To increase the vertical resolution and lateral continuity of the estimated reflectivity, we further improve the sparse-spike deconvolution by introducing the atomic norm minimization and structural regularization, respectively. Specifically, we use the atomic norm minimization to estimate the reflector locations, which are further used as position constraints in the sparse-spike deconvolution. By doing this, we can vertically separate highly thin layers through the sparse deconvolution. In addition, the seismic structural orientations are estimated from the seismic image to construct a structure-guided regularization in the deconvolution to preserve the lateral continuity of reflectivities. Our improvements are suitable for most types of sparse-spike deconvolution approaches. The sparse-spike deconvolution method with Toeplitz-sparse matrix factorization (TSMF) is used as an example to demonstrate the effectiveness of our improvements. Synthetic and real examples show that our methods perform better than TSMF in estimating the reflectivity of thin layers and preserving the lateral continuities.

FRFT Based Method to Estimate DOA for Wideband Signal

2013 ◽  
Vol 712-715 ◽  
pp. 2716-2720 ◽  
Author(s):  
Wei Yang ◽  
Yao Wu Shi
Keyword(s):  
Fractional Fourier Transform ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
New Method ◽  
New Technique ◽  
Signal Frequency ◽  
Signal To Noise ◽  
One Dimensional ◽  
Time Frequency ◽  
Better Than

This paper presents a new direction-of-arrival (DOA) estimation for wideband sources, using fractional Fourier transform with fitting angle (F3A). Unlike other coherent wideband methods, the new method does not require any preprocessing for initial values and decomposing into narrowband components. This new technique estimates DOA by rotating the time frequency plate with the fitting angle to fit the time frequency distribution approximately. The algorithm can be applied to arbitrary shaped one dimensional or two dimensional arrays. The signal frequency can be higher than the frequencies in many wideband algorithms. The performance of this wideband technique is compared with that of the new method through simulations. The simulations show that this new technique performs better than others, while this algorithm does not apparently vary with signal-to-noise ratio (SNR).

Sign in / Sign up

Export Citation Format

Share Document