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

Yue Cui; Junfeng Wang; Jie Qi; Zhanying Zhang; Jinqi Zhu

doi:10.3390/s19102383

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

Sensors ◽

10.3390/s19102383 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2383 ◽

Cited By ~ 4

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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Direction-of-Arrival Estimation for Coprime Array Using Compressive Sensing Based Array Interpolation

International Journal of Antennas and Propagation ◽

10.1155/2017/6425067 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Aihua Liu ◽

Qiang Yang ◽

Xin Zhang ◽

Weibo Deng

Keyword(s):

Covariance Matrix ◽

Degrees Of Freedom ◽

Linear Array ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Interpolation Algorithm ◽

Estimation Performance ◽

Sample Covariance ◽

Coprime Array

A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA) of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC) applied on the continuous lags in the VSNLA; the degrees of freedom (DoFs) for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI) is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA) with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.

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An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽

10.3390/s21134403 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4403

Author(s):

Ji Woong Paik ◽

Joon-Ho Lee ◽

Wooyoung Hong

Keyword(s):

Covariance Matrix ◽

Phased Array ◽

Null Space ◽

Estimation Method ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Improve Performance ◽

Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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Doa estimation by covariance matrix sparse reconstruction of coprime array

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2015.7178395 ◽

2015 ◽

Cited By ~ 18

Author(s):

Chengwei Zhou ◽

Zhiguo Shi ◽

Yujie Gu ◽

Nathan A. Goodman

Keyword(s):

Covariance Matrix ◽

Doa Estimation ◽

Sparse Reconstruction ◽

Coprime Array

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Off-grid DoA estimation method of wideband signals

International Journal of Communication Systems ◽

10.1002/dac.4466 ◽

2020 ◽

Vol 33 (14) ◽

pp. e4466

Author(s):

Yasoub Eghbali ◽

Mahmoud Ferdosizade Naeiny

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Wideband Signals

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High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

International Journal of Antennas and Propagation ◽

10.1155/2016/3743509 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Wenxing Li ◽

Xiaojun Mao ◽

Zhuqun Zhai ◽

Yingsong Li

Keyword(s):

Covariance Matrix ◽

High Performance ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Adaptive Beamforming ◽

Steering Vector ◽

Steering Vector Estimation ◽

Sample Covariance ◽

Noise Ratio ◽

Robust Adaptive

A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.

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Accurate and low-cost DOA estimation method using array covariance matrix elements

2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) ◽

10.1109/apccas.2014.7032836 ◽

2014 ◽

Cited By ~ 2

Author(s):

Yu Iwabuchi ◽

Koichi Ichige

Keyword(s):

Covariance Matrix ◽

Low Cost ◽

Estimation Method ◽

Doa Estimation ◽

Matrix Elements

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Covariance Matrix Reconstruction for Direction Finding with Nested Arrays Using Iterative Reweighted Nuclear Norm Minimization

International Journal of Antennas and Propagation ◽

10.1155/2019/7657898 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Weijie Tan ◽

Xi’an Feng

Keyword(s):

Covariance Matrix ◽

Regularization Parameter ◽

Estimation Method ◽

Estimation Algorithm ◽

Doa Estimation ◽

Direction Finding ◽

Nuclear Norm ◽

Low Rank ◽

Matrix Reconstruction ◽

Linear Transform

In this paper, we address the direction finding problem in the background of unknown nonuniform noise with nested array. A novel gridless direction finding method is proposed via the low-rank covariance matrix approximation, which is based on a reweighted nuclear norm optimization. In the proposed method, we first eliminate the noise variance variable by linear transform and utilize the covariance fitting criteria to determine the regularization parameter for insuring robustness. And then we reconstruct the low-rank covariance matrix by iteratively reweighted nuclear norm optimization that imposes the nonconvex penalty. Finally, we exploit the search-free DoA estimation method to perform the parameter estimation. Numerical simulations are carried out to verify the effectiveness of the proposed method. Moreover, results indicate that the proposed method has more accurate DoA estimation in the nonuniform noise and off-grid cases compared with the state-of-the-art DoA estimation algorithm.

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Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown Non-Uniform Noise

Mathematical Problems in Engineering ◽

10.1155/2018/3084516 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Hao Zhou ◽

Guoping Hu ◽

Junpeng Shi ◽

Ziang Feng

Keyword(s):

Covariance Matrix ◽

Degrees Of Freedom ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Main Diagonal ◽

Sample Covariance ◽

Uniform Noise ◽

Negative Effect ◽

Signal Covariance Matrix

Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

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