scholarly journals DOA estimation of the quasi‐stationary signal using sparse reconstruction

2021 ◽  
Author(s):  
Fan Wu ◽  
Fei Cao ◽  
Xurong Zhang ◽  
Shikun Lu ◽  
Yanhong Zhang
Keyword(s):  
Doa Estimation ◽  
Sparse Reconstruction

scholarly journals Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Riheng Wu ◽  
Yangyang Dong ◽  
Zhenhai Zhang ◽  
Le Xu
Keyword(s):  
Data Model ◽  
Doa Estimation ◽  
Nonsingular Matrix ◽  
Eigenvalue Decomposition ◽  
Estimation Methods ◽  
Sparse Reconstruction ◽  
Invariance Property ◽  
Reconstruction Method ◽  
Uniqueness Of Solutions ◽  
Moderate Complexity

We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.

scholarly journals Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1056 ◽  
Author(s):  
Min Han ◽  
Wenbin Dou
Keyword(s):  
Doa Estimation ◽  
Spatial Domain ◽  
Estimation Problem ◽  
Semidefinite Program ◽  
Sparse Reconstruction ◽  
Reconstruction Problem ◽  
Estimation Performance ◽  
Atomic Norm ◽  
Dual Polarized ◽  
Definition Of

In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

2019 ◽  
Vol 8 (4) ◽  
pp. 1004-1007 ◽  
Author(s):  
Yuexian Wang ◽  
Ling Wang ◽  
Jian Xie ◽  
Matthew Trinkle ◽  
Brian W.-H. Ng
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Linear Arrays

scholarly journals High Precision Sparse Reconstruction Scheme for Multiple Radar Mainlobe Jammings

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1224
Author(s):  
Yuan Cheng ◽  
Daiyin Zhu ◽  
Jindong Zhang
Keyword(s):  
High Precision ◽  
Traditional Method ◽  
Bayesian Learning ◽  
Estimation Error ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Sparse Bayesian Learning ◽  
Oblique Projection ◽  
Direction Estimation ◽  
Reconstruction Scheme

Radar mainlobe jamming has attracted considerable attention in the field of electronic countermeasures. When the direction of arrival (DOA) of jamming is close to that of the target, the conventional antijamming methods are ineffective. Generally, mainlobe antijamming method based on blind source separation (BSS) can deteriorate the target direction estimation. Thus in this paper, a high precision sparse reconstruction scheme for multiple radar mainlobe jammings is proposed that does not suffer from failure or performance degradation inherent in the traditional method. First, the mainlobe jamming signal and desired signal components are extracted by using the joint approximation diagonalization of eigenmatrices (JADE) method. Then, oblique projection with sparse Bayesian learning (OP-SBL) method is employed to reconstruct the target with high precision. The proposed method is capable of suppressing at most three radar mainlobe jammers adaptively and also obtain DOA estimation error less than 0.1°. Simulation and experimental results confirm the effectiveness of the proposed method.

scholarly journals A Novel 2-D Coherent DOA Estimation Method Based on Dimension Reduction Sparse Reconstruction for Orthogonal Arrays

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1496 ◽  
Author(s):  
Xiuhong Wang ◽  
Xingpeng Mao ◽  
Yiming Wang ◽  
Naitong Zhang ◽  
Bo Li
Keyword(s):  
Dimension Reduction ◽  
Estimation Method ◽  
Orthogonal Arrays ◽  
Doa Estimation ◽  
Sparse Reconstruction

scholarly journals Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2383 ◽  
Author(s):  
Yue Cui ◽  
Junfeng Wang ◽  
Jie Qi ◽  
Zhanying Zhang ◽  
Jinqi Zhu
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Sparse Matrix ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Virtual Sensors ◽  
Steering Vector ◽  
Wideband Signals ◽  
Variant Frequency

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.

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