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 A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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

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.

An Improved Direction-of-Arrival Estimation Method Based on Sparse Reconstruction

2012 ◽  
Vol 263-266 ◽  
pp. 135-138
Author(s):  
Xue Bing Han ◽  
Zhao Jun Jiang
Keyword(s):  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Direction Of Arrival Estimation ◽  
Sensing Matrix ◽  
Signal Model ◽  
Multiple Measurement Vectors ◽  
Underdetermined System ◽  
Simulation Results

In this paper, we account for efficient approach of direction-of-arrival estimation based on sparse reconstruction of sensor measurements with an overcomplete basis. MSD-FOCUSS ( MMV Synchronous Descending FOCal Underdetermined System Solver) algorithm is developed against to sparse reconstruction in multiple-measurement-vectors (MMV) system where noise perturbations exist in both the measurements and sensing matrix. The paper shows how sparse-signal model of DOA estimation is established and MSD-FOCUSS is derived, then the simulation results illustrate the advantage of MSD-FOCUSS when it is used to solve the problem of DOA estimation.

scholarly journals An Efficient Direction of Arrival Estimation Algorithm for Sources with Intersecting Signature in the Time–Frequency Domain

2021 ◽  
Vol 11 (4) ◽  
pp. 1849 ◽  
Author(s):  
Nabeel Ali Khan ◽  
Sadiq Ali ◽  
Kwonhue Choi
Keyword(s):  
Frequency Domain ◽  
Instantaneous Frequency ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Direction Of Arrival Estimation ◽  
Localization Accuracy ◽  
Time Frequency ◽  
Multiple Sensors ◽  
Sensors Array

An efficient direction of arrival estimation method is proposed. The proposed algorithm accurately estimates the instantaneous frequency of signals received by multiple sensors (array of sensors/antennas). The estimated instantaneous frequency is then used to separate sources and estimate their direction of arrivals. Experimental results indicate that the proposed method achieves better performance than the existing methods both in terms of computational requirements and localization accuracy. It is also shown that the proposed method can work in under-determined situations.

scholarly journals DOA Estimation of Wideband Cyclic MUSIC Algorithm under Rayleigh Fading Environment in MIMO Systems

2020 ◽  
Vol 9 (1) ◽  
pp. 1834-1837
Keyword(s):  
High Resolution ◽  
Rayleigh Fading ◽  
Array Signal Processing ◽  
Mimo Systems ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Direction Of Arrival Estimation ◽  
Music Algorithm

Direction of arrival (DOA) estimation has, for quite some time been a challenging situation in most of the wireless communication applications, radar and sonar. The resolution of the direction of arrival estimation can be increased using the help of array signal processing. The performance of the direction of arrival estimation for multiple input multiple output(MIMO) radar systems has been reviewed for cyclic Multiple Signal Classification(MUSIC), extended cyclic MUSIC and Wideband cyclic MUSIC under Rayleigh fading environment. MUSIC and its variants have been taken into consideration for the analysis as these have been a very good parameter estimation technique due its low cost, high resolution and stability. Direction of Arrival estimation clubbed with cyclostationarity has been included into the new algorithm because of its immunity to noise and interference. The new algorithm along with cyclic correlations when applied to these signals, improves the performance of the entire system substantially. The performance of this wideband cyclic MUSIC high resolution direction of arrival estimation algorithm over the Rayleigh fading is analyzed in this paper. The simulation results citing the three methods show the performance of these methods in presence of the fading environments.

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 Direction of Arrival Estimation Using Augmentation of Coprime Arrays

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 277 ◽  
Author(s):  
Tehseen Hassan ◽  
Fei Gao ◽  
Babur Jalal ◽  
Sheeraz Arif
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Multiple Signal Classification ◽  
Coprime Arrays ◽  
The Difference ◽  
Source Signals

Recently, direction of arrival (DOA) estimation premised on the sparse arrays interpolation approaches, such as co-prime arrays (CPA) and nested array, have attained extensive attention because of the effectiveness and capability of providing higher degrees of freedom (DOFs). The co-prime array interpolation approach can detect O(MN) paths with O(M + N) sensors in the array. However, the presence of missing elements (holes) in the difference coarray has limited the number of DOFs. To implement co-prime coarray on subspace based DOA estimation algorithm namely multiple signal classification (MUSIC), a reshaping operation followed by the spatial smoothing technique have been presented in the literature. In this paper, an active coarray interpolation (ACI) is proposed to efficiently recovering the covariance matrix of the augmented coarray from the original covariance matrix of source signals with no vectorizing and spatial smoothing operation; thus, the computational complexity reduces significantly. Moreover, the numerical simulations of the proposed ACI approach offers better performance compared to its counterparts.

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.

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