scholarly journals Highly Accurate Multi-Invariance ESPRIT for DOA Estimation with a Sparse Array

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chen Gu ◽  
Hong Hong ◽  
Yusheng Li ◽  
Xiaohua Zhu ◽  
Jin He
Keyword(s):  
Doa Estimation ◽  
Invariance Property ◽  
Two Dimensional ◽  
Sparse Array ◽  
Half Wavelength ◽  
Vector Sensors ◽  
Matrix Blocks ◽  
Simulation Results ◽  
Non Gaussian ◽  
Array Geometry

This paper proposes a multi-invariance ESPRIT-based method for estimation of 2D direction (MIMED) of multiple non-Gaussian monochromatic signals using cumulants. In the MIMED, we consider an array geometry containing sparse L-shaped diversely polarized vector sensors plus an arbitrarily-placed single polarized scalar sensor. Firstly, we define a set of cumulant matrices to construct two matrix blocks with multi-invariance property. Then, we develop a multi-invariance ESPRIT-based algorithm with aperture extension using the defined matrix blocks to estimate two-dimensional directions of the signals. The MIMED can provide highly accurate and unambiguous direction estimates by extending the array element spacing beyond a half-wavelength. Finally, we present several simulation results to demonstrate the superiority of the MIMED.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

Effect of Sparse Array Geometry on Estimation of Co-array Signal Subspace

2021 ◽  
Vol 35 (11) ◽  
pp. 1435-1436
Author(s):  
Mehmet Hucumenoglu ◽  
Piya Pal
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Doa Estimation ◽  
Singular Value ◽  
Signal Subspace ◽  
Sparse Array ◽  
Subspace Estimation ◽  
Signal Covariance Matrix ◽  
Special Case ◽  
Array Geometry

This paper considers the effect of sparse array geometry on the co-array signal subspace estimation error for Direction-of-Arrival (DOA) estimation. The second largest singular value of the signal covariance matrix plays an important role in controlling the distance between the true subspace and its estimate. For a special case of two closely-spaced sources impinging on the array, we explicitly compute the second largest singular value of the signal covariance matrix and show that it can be significantly larger for a nested array when compared against a uniform linear array with same number of sensors.

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 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.

scholarly journals Joint DOA and Polarization Estimation with Two Parallel Sparse Dipole Arrays

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Xie ◽  
Ling Wang ◽  
Zhaolin Zhang
Keyword(s):  
Parameter Estimation ◽  
Mutual Coupling ◽  
Direction Cosine ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Invariance Property ◽  
Array Configuration ◽  
Vector Sensors ◽  
Array Aperture ◽  
Parallel Arrays

Electromagnetic vector sensors (EMVS) have attracted growing attention in recent years. However, the mutual coupling effects in practical EMVS arrays may seriously degrade the parameter estimation performance. In order to solve this problem, a novel array configuration consisting of two parallel sparse dipole arrays is proposed. Based on the spatially rotational invariance property between the two parallel arrays and the interdipole spacing inside each array, highly accurate but ambiguous direction-cosine estimates, coarse direction-of-arrival (DOA) estimates, and polarization parameter estimation can be obtained jointly. The coarse DOA estimates are then employed to disambiguate the phase ambiguities in the fine estimates. Compared with collocated EMVS, the proposed array overcomes the mutual coupling problem. Moreover, the DOA estimation accuracy is promoted due to the sparse array aperture extension. Simulation results demonstrate the effectiveness of the proposed algorithm.

An Effective CS-BP Algorithm for 2-D DOA Estimation

2011 ◽  
Vol 128-129 ◽  
pp. 62-65
Author(s):  
Yu Yan An ◽  
Sen Sen Bai
Keyword(s):  
Frequency Modulation ◽  
Sampling Rate ◽  
Doa Estimation ◽  
Bp Algorithm ◽  
Basis Pursuit ◽  
Two Dimensional ◽  
Digital Converter ◽  
Analog To Digital ◽  
High Sampling Rate ◽  
Simulation Results

The bandwidth of the direction of arrival (DOA) estimation requires high sampling rate which extends the current analog to digital converter capacity. The paper presents an effective basis pursuit (BP) algorithm based on compressive sensing (CS) signals, which is called CS-BP algorithm, for two-dimensional (2-D) DOA estimation of Linear Frequency Modulation (LFM ). The simulation results verified that the method can effectively reduce the sampling data, and improve DOA estimation performance and efficiency.

scholarly journals Sparse Array Design for DOA Estimation of Non-Gaussian Signals: From Global Postage-Stamp Problem Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Changbo Ye ◽  
Luo Chen ◽  
Beizuo Zhu
Keyword(s):  
Design Problem ◽  
Doa Estimation ◽  
Second Order ◽  
Sensor Location ◽  
Postage Stamp ◽  
Array Design ◽  
Sparse Array ◽  
Physical Sensors ◽  
Physical Sensor ◽  
Non Gaussian

In this paper, a sparse array design problem for non-Gaussian signal direction of arrival (DOA) estimation is investigated. Compared with conventional second-order cumulant- (SOC-) based methods, fourth-order cumulant- (FOC-) based methods achieve improved DOA estimation performance by utilizing all information from received non-Gaussian sources. Considering the virtual sensor location of vectorized FOC-based methods can be calculated from the second order difference coarray of sum coarray (2-DCSC) of physical sensors, it is important to devise a sparse array design principle to obtain extended degree of freedom (DOF). Based on the properties of unfolded coprime linear array (UCLA), we formulate the sparse array design problem as a global postage-stamp problem (GPSP) and then present an array design method from GPSP perspective. Specifically, for vectorized FOC-based methods, we divide the process of obtaining physical sensor location into two steps; the first step is to obtain the two consecutive second order sum coarrays (2-SC), which can be modeled as GPSP, and the solutions to GPSP can also be utilized to determine the physical sensor location sets without interelement spacing coefficients. The second step is to adjust the physical sensor sets by multiplying the appropriate coprime coefficients, which is determined by the structure of UCLA. In addition, the 2-DCSC can be calculated from physical sensors directly, and the properties of UCLA are given to confirm the degree of freedom (DOF) of the proposed geometry. Simulation results validate the effectiveness and superiority of the proposed array geometry.

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 Research on Ambiguity Resolution Algorithm by Quaternion Based on Acoustic Vector Sensor

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guibao Wang ◽  
Xinkuan Wang ◽  
Lanmei Wang ◽  
Xiangyu Wang
Keyword(s):  
Ambiguity Resolution ◽  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Velocity ◽  
Phase Ambiguity ◽  
Acoustic Vector Sensor ◽  
Vector Sensor ◽  
Half Wavelength ◽  
Simulation Results ◽  
Resolution Algorithm

The increase in element spacing can increase the aperture of the array and improve its resolution performance. However, phase ambiguity will occur when the array element interval is larger than the minimum half wavelength of the incident signal. The three acoustic velocity components of the acoustic vector are ingeniously constructed into a new kind of quaternions because of the special structure of the acoustic vector sensor array, and the rough estimation of the direction of arrival (DOA) is obtained using the rotation relationship between the subarray steering vectors corresponding to quaternion data. The rough estimate is used to resolve the phase ambiguity of the spatial phase difference between the array elements, and the high-precision DOA estimation of the signal can be obtained. Simulation results show that the method is effective.

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