Simulation and Implementation of 2D DOA Estimation in Wireless Location

2012 ◽  
Vol 195-196 ◽  
pp. 661-665
Author(s):  
Ping Tan ◽  
Zhi Yao Zhou ◽  
Yu Feng Zhang ◽  
Ye Luo ◽  
Hong Ma
Keyword(s):  
High Resolution ◽  
Unitary Transformation ◽  
Measurement Data ◽  
Doa Estimation ◽  
High Accuracy ◽  
Two Dimensional ◽  
Circular Array ◽  
Music Algorithm ◽  
Wireless Location ◽  
2D Doa Estimation

It is needed to realize high resolution two dimensional (2D) direction of arrivals (DOA) estimation in determining the location of the mobile with high accuracy. In this paper, the problem of estimating the 2D DOA using uniform circular array (UCA) is investigated. Performance of 2D DOA estimation based on the real-valued unitary transformation MUSIC algorithm for UCA is presented, especially focusing on DOA estimation of multiple correlated signals.Then the validations of Unitary Transformation MUSIC algorithm are performed based on the measurement data in a wireless location system.

scholarly journals Computationally Efficient Ambiguity-Free Two-Dimensional DOA Estimation Method for Coprime Planar Array: RD-Root-MUSIC Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Luo Chen ◽  
Changbo Ye ◽  
Baobao Li
Keyword(s):  
Degrees Of Freedom ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Estimation Algorithms ◽  
Planar Array ◽  
2D Doa Estimation

While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm.

Two-Dimensional Direction Finding Estimation for Uniform Rectangular Array with Unknown Mutual Coupling via Real-Valued PARAFAC Decomposition

2019 ◽  
Vol 28 (03) ◽  
pp. 1950049
Author(s):  
Lingyun Xu ◽  
Fangqing Wen
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Rectangular Array ◽  
Lower Complexity ◽  
2D Doa Estimation ◽  
Esprit Algorithm ◽  
Parafac Decomposition ◽  
Uniform Rectangular Array

Two-dimensional direction-of-arrival (2D-DOA) estimation for uniform rectangular array (URA) is a canonical problem with numerous applications, e.g., wireless communications, sonar and radar systems. The conventional 2D-DOA estimators usually are derived with the assumption of ideal arrays. However, in practice, the arrays may not be well calibrated and suffer from unknown mutual coupling. Using the conventional estimators may lead to low accuracy estimation and high computational complexity in the condition of large number of array elements. In this paper, a novel real-valued parallel factor (PARAFAC) decomposition algorithm is proposed to tackle this problem. The proposed algorithm has better angle estimation performance than the multiple signal classification (MUSIC) algorithm, estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PARAFAC algorithm. But it has lower complexity than MUSIC algorithm. Moreover, the proposed algorithm can obtain automatically paired 2D-DOA estimation, and it is suitable to coherent or closely spaced signals and can eliminate the mutual coupling. Simulation results verify the effectiveness of the proposed algorithm.

A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF

2014 ◽  
Vol 610 ◽  
pp. 339-344
Author(s):  
Qiang Guo ◽  
Yun Fei An
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Real Time ◽  
Unitary Transformation ◽  
Wiener Filter ◽  
Doa Estimation ◽  
Signal Subspace ◽  
Music Algorithm ◽  
Multi Stage ◽  
Simulation Results

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

scholarly journals A FPC-ROOT Algorithm for 2D-DOA Estimation in Sparse Array

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Wenhao Zeng ◽  
Hongtao Li ◽  
Xiaohua Zhu ◽  
Chaoyu Wang
Keyword(s):  
Null Space ◽  
Matrix Completion ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Polynomial Roots ◽  
Sparse Array ◽  
Autocorrelation Matrix ◽  
Proposed Model ◽  
The Matrix ◽  
2D Doa Estimation

To improve the performance of two-dimensional direction-of-arrival (2D DOA) estimation in sparse array, this paper presents a Fixed Point Continuation Polynomial Roots (FPC-ROOT) algorithm. Firstly, a signal model for DOA estimation is established based on matrix completion and it can be proved that the proposed model meets Null Space Property (NSP). Secondly, left and right singular vectors of received signals matrix are achieved using the matrix completion algorithm. Finally, 2D DOA estimation can be acquired through solving the polynomial roots. The proposed algorithm can achieve high accuracy of 2D DOA estimation in sparse array, without solving autocorrelation matrix of received signals and scanning of two-dimensional spectral peak. Besides, it decreases the number of antennas and lowers computational complexity and meanwhile avoids the angle ambiguity problem. Computer simulations demonstrate that the proposed FPC-ROOT algorithm can obtain the 2D DOA estimation precisely in sparse array.

scholarly journals Direction of Arrival Estimation of Uncorrelated Signals Using Root-MUSIC Algorithm for ULAs and UCAs

2018 ◽  
Vol 7 (4.36) ◽  
pp. 398
Author(s):  
S. Venkata Rama Rao ◽  
A. Mallikarjuna Prasad ◽  
Ch. Santhi Rani
Keyword(s):  
Basic Problem ◽  
Signal To Noise Ratio ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Music Algorithm ◽  
Linear Arrays ◽  
Circular Arrays ◽  
Symmetric Structure ◽  
The Individual

In this paper, Root-MUSIC algorithm for direction of arrival (DOA) estimation of uncorrelated signals is explored both for uniform linear and uniform circular arrays. The basic problem in Uniform Linear Arrays (ULAs) is Mutual coupling between the individual elements of the antenna array. This problem is reduced in Uniform Circular Arrays (UCAs) because of its symmetric structure. The DOA estimation of uncorrelated signals that have different power levels is simulated on a MATLAB environment. And the noise consider is white across all the array elements. The factors considered for simulation are number of number of snapshots, array elements, radius of circular array, array length, and signal to noise ratio. 

scholarly journals An APG-MUSIC Algorithm Based-on Optimized Sampling Array

2018 ◽  
Vol 208 ◽  
pp. 01004
Author(s):  
Mengxia Li ◽  
Wen Hu ◽  
Jiaying Di ◽  
Hongtao Li
Keyword(s):  
Null Space ◽  
Matrix Completion ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Sampling ◽  
Spatial Spectrum ◽  
Low Rank ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
2D Doa Estimation

This paper proposes a novel two-dimensional direction of arrival (2D-DOA) estimation with optimized sparse sampling array, which is combined with Accelerated Proximal Gradient singular value thresholding(APG) and Multiple Signal Classification(MUSIC). Firstly, a signal model of 2D-DOA estimation in sparse array is established, which is proved to satisfy low rank feature and NULL Space Property(NSP). Then, Genetic algorithm (GA) is applied to a sparse sampling array to optimize the performance of matrix completion(MC). Finally, MUSIC combined with APG is studied to recover received signal matrix and estimate the direction of arrival. The results of computer simulation demonstrate that compared with conventional 2D-DOA algorithms, the proposed algorithm reduces the number of array elements needed dramatically and effectively lowers the average sidelobes level of spatial spectrum.

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