Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

2015 ◽  
Vol 23 (04) ◽  
pp. 1540007 ◽  
Author(s):  
Guolong Liang ◽  
Wenbin Zhao ◽  
Zhan Fan
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Data Simulation ◽  
Estimation Algorithms ◽  
Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

scholarly journals Direction-of-Arrival Estimation of Far-Field Sources Under Near-Field Interferences in Passive Sonar Array

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28413-28420
Author(s):  
Hojun Lee ◽  
Jongmin Ahn ◽  
Yongcheol Kim ◽  
Jaehak Chung
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Passive Sonar

scholarly journals AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zhi-Chao Sha ◽  
Zhang-Meng Liu ◽  
Zhi-Tao Huang ◽  
Yi-Yu Zhou
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Coherent Signals ◽  
Model Based ◽  
Mixing Matrix ◽  
Simulation Results

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

scholarly journals COPRIME ARRAY PARAMETERS OPTIMIZATION FOR DOA ESTIMATION

2021 ◽  
Vol 4 (2) ◽  
pp. 23-32
Author(s):  
Fatimah A. Salman ◽  
Bayan M. Sabbar
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Parameters Optimization ◽  
Degree Of Freedom ◽  
Direction Of Arrival Estimation ◽  
Sparse Arrays ◽  
Sparse Array ◽  
Simulation Results ◽  
Coprime Array

Sparse array such as the coprime array is one of the most preferable sparse arrays for direction of arrival estimation due to its properties, like simplicity, capability of resolving more sources than the number of elements and resistance to mutual coupling issue.  In this paper, a new coprime array model is proposed to increase the number of degree of freedom (DOF) and improve the performance of coprime array.   The new designed array can avoid the mutual coupling by minimizing the lag redundancy and expand the central lags in the virtual difference co-array. Thus, the propose structure can resolve more sources than the prototype coprime array using the same number of elements with improved direction of arrival estimation. Simulation results demonstrate that the proposed array model is more efficient than the others coprime array model.

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 Robust Direction of Arrival Estimation Using Uniform Circular Antenna Array based on Total Forward - Backward Matrix Pencil Method

2015 ◽  
pp. 17
Author(s):  
Han Trong Thanh ◽  
Do Trong Tuan ◽  
Nguyen Trong Duc ◽  
Vu Van Yem
Keyword(s):  
Matrix Pencil ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Observation Angle ◽  
Direction Of Arrival Estimation ◽  
Linear Antenna ◽  
Matrix Pencil Method ◽  
Signal Simulation ◽  
Simulation Results ◽  
Circular Antenna Array

In  this  paper,  we  propose  an  approach  to estimate  the  Direction  of  Arrival  (DOA)  of  Radio coherent  incoming  signals  using  the  Total  Forward  – Backward  Matrix  Pencil  algorithm  (TFBMP).  This algorithm  works  directly  on  samples  of  signals impinging  on  an  M  –  element  Uniform  Circular Antenna (UCA) array, which has a smaller size as well as  larger  observation  angle  in  comparison  with  the Uniform  Linear  Antenna  (ULA)  array.  Therefore,  the correlation  between  the  received  signals  does  not significantly  impact  on  its performance  and  efficiency. Furthermore,  this algorithm  can  also  extract  the  DOA information  with  only  one  snapshot  of  signal. Simulation  results  for  DOA  estimation  using  the proposed approach for different situations of  incoming signals  as  well  as  the  number  of  snapshots  in  the presence  of  noise  will  be  assessed  to  verify  its performance.

scholarly journals A Sparse Perspective for Direction-of-Arrival Estimation Under Strong Near-Field Interference Environment

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 163
Author(s):  
Longhao Qiu ◽  
Tian Lan ◽  
Yilin Wang
Keyword(s):  
Near Field ◽  
Spherical Wave ◽  
Reconstruction Algorithm ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Noise Power ◽  
Far Field ◽  
Music Algorithm ◽  
Practical Applications ◽  
Minimum Variance Distortionless Response

Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.

scholarly journals Sparse Cumulants Fitting for Direction-of-Arrival Estimation without Redundancy

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shuang Li ◽  
Xiaoxiao Jiang ◽  
Sai Ma ◽  
Yingguan Wang
Keyword(s):  
Gaussian Noise ◽  
Software Package ◽  
A Priori ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Array Output

A novel direction-of-arrival (DOA) estimation method is proposed based on the sparse cumulants fitting without redundancy. Firstly, we derive that some fourth order cumulants of the array output are redundant and therefore are removed to reduce computational complexity. Then, the left cumulants are sparsely represented on an overcomplete basis and the DOAs are resolved by using a software package. Despite introducing a high variance, the proposed method shows several advantages including the ability to detect more sources than sensors, high resolution, and robustness to all kinds of Gaussian noise. Besides, our method does not have to know, a priori, the number of sources. Simulation results are presented to illustrate the effectiveness and efficiency of the proposed method.

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.

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