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

2014 ◽  
Author(s):  
Yu Iwabuchi ◽  
Koichi Ichige
Keyword(s):  
Covariance Matrix ◽  
Low Cost ◽  
Estimation Method ◽  
Doa Estimation ◽  
Matrix Elements

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

Sensors ◽  
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.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
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.

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.

New DOA Estimation Method for Wideband Coherent Signals in the Presence of Correlated Noise

2012 ◽  
Vol 263-266 ◽  
pp. 157-161 ◽  
Author(s):  
Jin Zhang ◽  
Yun Xiang Mao ◽  
Jian Yun Zhang
Keyword(s):  
Numerical Simulation ◽  
Antenna Array ◽  
Covariance Matrix ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Correlated Noise ◽  
Linear Antenna ◽  
Coherent Signals ◽  
Linear Antenna Array

With a uniform linear antenna array, a new direction-of-arrival (DOA) estimation method is proposed for wideband coherent signals in the presence of unknown correlated noise but with structured covariance matrix. Based on this proposed structure, i.e. Hermitian Toeplitz, a spatial differencing operation that exploits this symmetry is applied to remove the effect of the unknown noise and a new matrix is constructed accordingly at each frequency bin. Following this step, a focusing operation is performed to give the corresponding aligned covariance matrix. Finally, an eigenstructure-based DOA estimation method is applied. The validity of the method is supported by numerical simulation under various conditions.

scholarly journals A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3025 ◽  
Author(s):  
Weijian Si ◽  
Fuhong Zeng ◽  
Changbo Hou ◽  
Zhanli Peng
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sample Covariance Matrix ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Estimation Process ◽  
Sample Covariance ◽  
Coprime Arrays

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

scholarly journals Gridless DOA Estimation for Minimum-Redundancy Linear Array in Nonuniform Noise

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Changyun Qi ◽  
Gong Zhang ◽  
Jiawen Yuan
Keyword(s):  
Covariance Matrix ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Residual Energy ◽  
Estimation Accuracy ◽  
Two Stages ◽  
Signal Covariance Matrix ◽  
Toeplitz Structure ◽  
True Signal

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
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.

scholarly journals Underdetermined DOA Estimation for Wideband Signals via Focused Atomic Norm Minimization

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 359 ◽  
Author(s):  
Juan Shi ◽  
Qunfei Zhang ◽  
Weijie Tan ◽  
Linlin Mao ◽  
Lihuan Huang ◽  
...  
Keyword(s):  
Information Theory ◽  
Signal Processing ◽  
Covariance Matrix ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Virtual Array

In underwater acoustic signal processing, direction of arrival (DOA) estimation can provide important information for target tracking and localization. To address underdetermined wideband signal processing in underwater passive detection system, this paper proposes a novel underdetermined wideband DOA estimation method equipped with the nested array (NA) using focused atomic norm minimization (ANM), where the signal source number detection is accomplished by information theory criteria. In the proposed DOA estimation method, especially, after vectoring the covariance matrix of each frequency bin, each corresponding obtained vector is focused into the predefined frequency bin by focused matrix. Then, the collected averaged vector is considered as virtual array model, whose steering vector exhibits the Vandermonde structure in terms of the obtained virtual array geometries. Further, the new covariance matrix is recovered based on ANM by semi-definite programming (SDP), which utilizes the information of the Toeplitz structure. Finally, the Root-MUSIC algorithm is applied to estimate the DOAs. Simulation results show that the proposed method outperforms other underdetermined DOA estimation methods based on information theory in term of higher estimation accuracy.

Sign in / Sign up

Export Citation Format

Share Document