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.

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.

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 DOA Estimation of Noncircular Signals Using Quaternions

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Tianzhen Meng ◽  
Minjie Wu ◽  
Naichang Yuan
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Output Vector ◽  
Noncircular Signals ◽  
Simulation Results

The two-dimensional (2D) direction-of-arrival (DOA) estimation problem for noncircular signals using quaternions is considered in this paper. In the framework of quaternions, we reconstruct the conjugate augmented output vector which reduces the dimension of covariance matrix. Compared with existing methods, the proposed one has two main advantages. Firstly, the estimation accuracy is higher since quaternions have stronger orthogonality. Secondly, the dimension of covariance matrix is reduced by half which decreases the computational complexity. Simulation results are presented verifying the efficacy of the algorithm.

scholarly journals Blind Direction-of-Arrival Estimation with Uniform Circular Array in Presence of Mutual Coupling

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Song Liu ◽  
Lisheng Yang ◽  
Shizhong Yang ◽  
Qingping Jiang ◽  
Haowei Wu
Keyword(s):  
Mutual Coupling ◽  
Coupling Parameter ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Circular Array ◽  
Signal Parameters ◽  
Simulation Results ◽  
Parameter Estimation Methods

A blind direction-of-arrival (DOA) estimation algorithm based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed for a uniform circular array (UCA) when strong electromagnetic mutual coupling is present. First, an updated UCA model with mutual coupling in a discrete Fourier transform (DFT) beam space is deduced, and the new manifold matrix is equal to the product of a centrosymmetric diagonal matrix and a Vandermonde-structure matrix. Then we carry out blind DOA estimation through a modified ESPRIT method, thus avoiding the need for spatial angular searching. In addition, two mutual coupling parameter estimation methods are presented after the DOAs have been estimated. Simulation results show that the new algorithm is reliable and effective especially for closely spaced signals.

scholarly journals TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 220
Author(s):  
Liyu Lin ◽  
Chaoran She ◽  
Yun Chen ◽  
Ziyu Guo ◽  
Xiaoyang Zeng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Cross Entropy ◽  
Data Driven ◽  
Estimation Accuracy ◽  
Model Based ◽  
Classification And Regression ◽  
Grid Based

For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB.

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.

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