scholarly journals A Fast Estimation Method for Direction of Arrival Using Tripole Vector Antenna

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 5008
Author(s):  
Bodong Zhang ◽  
Xuan Zou ◽  
Tingyi Zhang ◽  
Yunong Tang ◽  
Hao Zeng
Keyword(s):  
Electric Field ◽  
Estimation Error ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Cross Product ◽  
Statistical Characteristics ◽  
Unbiased Estimation ◽  
Vector Antenna ◽  
Product Vector

The tripole vector antenna comprises three orthogonal dipole antennas, so it could completely capture all the electric field of the incident electromagnetic (EM) wave. Then, the electric field information could be used to estimate the direction of arrival (DOA) of the EM wave if two conditions are satisfied. One is that there exists only one single EM wave in space. The other is that the EM wave is elliptically or circularly polarized. The new estimation method obtains two snapshot vectors through the output of a tripole antenna and computes their cross-product vector. Furthermore, the direction of the cross-product vector is used to estimate the DOA of the EM wave directly. We analyze the statistical characteristics of the DOA estimation error to prove that the new scheme is an asymptotic unbiased estimation. Furthermore, unlike the existing Multiple Signal Classification (MUSIC)-based algorithms, the proposed approach only need one tripole vector antenna instead of an antenna array. Meanwhile, the new method also outperforms existing MUSIC-based algorithms in the term of computational complexity. Finally, the performance and advantages of the proposed method are verified by numerical simulations.

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 DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique

2018 ◽  
Vol 232 ◽  
pp. 01012
Author(s):  
Bo Xu ◽  
Zhigang Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Circular Arrays ◽  
Noise Ratio ◽  
Subarray Beamforming ◽  
Beamforming Technique

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques.

scholarly journals Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2788 ◽  
Author(s):  
Yuehao Guo ◽  
Xianpeng Wang ◽  
Wensi Wang ◽  
Mengxing Huang ◽  
Chong Shen ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Direction Of Departure ◽  
Value Decomposition

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

2020 ◽  
Author(s):  
Caiyi Tang ◽  
Qianli Wang ◽  
Zhiqin Zhao ◽  
Zaiping Nie ◽  
Chuanfeng Niu
Keyword(s):  
Processing Time ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Transformation Technique ◽  
Computational Burden ◽  
First Time ◽  
Single Snapshot

Abstract Sparse Bayesian learning (SBL) has been successfully applied in solving the problem of direction-of-arrival (DOA) estimation. However, SBL needs multiple snapshots to ensure accuracy and costs huge computational workload. To reduce the requirement of snapshot and computational burden, a DOA estimation method based on the randomize-then-optimize (RTO) algorithm is first time introduced. RTO algorithm uses the optimization and Metropolis-Hastings approach to avoid the “learning” process of SBL in updating hyperparameters. And in order to apply RTO algorithm in the circumstance of signal with Laplace prior, a prior transformation technique is first induced. Compared with conventional CS based DOA methods, the proposed method has a better accuracy with single snapshot and shorter processing time. Some simulations are conducted to demonstrate the effectiveness of the proposed method.

scholarly journals Direction-of-Arrival Estimation of Virtual Array Signals Based on Doppler Effect

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Zhao ◽  
Xia Hao ◽  
Hongbin Chen
Keyword(s):  
High Speed ◽  
Estimation Method ◽  
High Mobility ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Music Algorithm ◽  
Moving Sources ◽  
Direction Of Departure ◽  
Virtual Array

The estimation accuracy of direction-of-departure (DOD) and direction-of-arrival (DOA) is reduced because of Doppler shifts caused by the high-speed moving sources. In this paper, an improved DOA estimation method which combines the forward-backward spatial smoothing (FBSS) technique with the MUSIC algorithm is proposed for virtual MIMO array signals in high mobility scenarios. Theoretical analysis and experiment results demonstrate that the resolution capability can be significantly improved by using the proposed method compared to the MUSIC algorithm for the moving sources with limited array elements, especially the DOA which can still be accurately estimated when the sources are much closely spaced.

scholarly journals Accurate Direction-of-Arrival Estimation Method based on Space-Time Modulated Metasurface

2022 ◽  
Author(s):  
Mengmeng Li
Keyword(s):  
Plane Wave ◽  
Incident Angle ◽  
Estimation Method ◽  
Time Estimation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Space Time ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Hardware Complexity

In this paper, we present a metasurface-based Direction of Arrival (DoA) estimation method that exploits the properties of space-time modulated reflecting metasurfaces to estimate in real-time the impinging angle of an illuminating monochromatic plane wave. The approach makes use of the amplitude unbalance of the received fields at broadside at the frequencies of the two first-order harmonics generated by the interaction between the incident plane wave and the modulated metasurface. Here, we first describe analytically how to generate the desired higher-order harmonics in the reflected spectrum and how to realize the breaking of the spatial symmetry of each order harmonic scattering pattern. Then, the one dimensional (1D) omnidirectional incident angle can be analytically computed using +1st and -1st order harmonics. The approach is also extended to 2D DoA estimation by using two orthogonally arranged 1D DoA modulation arrays. The accuracy of 1D DoA estimation is verified through full-wave numerical simulations. Compared to conventional DoA estimation methods, the proposed approach simplifies the computation and hardware complexity, ensuring at the same time estimation accuracy. The proposed method may have potential applications in wireless communications, target recognition, and identification.

scholarly journals Accurate Direction-of-Arrival Estimation Method based on Space-Time Modulated Metasurface

2022 ◽  
Author(s):  
Mengmeng Li
Keyword(s):  
Plane Wave ◽  
Incident Angle ◽  
Estimation Method ◽  
Time Estimation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Space Time ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Hardware Complexity

In this paper, we present a metasurface-based Direction of Arrival (DoA) estimation method that exploits the properties of space-time modulated reflecting metasurfaces to estimate in real-time the impinging angle of an illuminating monochromatic plane wave. The approach makes use of the amplitude unbalance of the received fields at broadside at the frequencies of the two first-order harmonics generated by the interaction between the incident plane wave and the modulated metasurface. Here, we first describe analytically how to generate the desired higher-order harmonics in the reflected spectrum and how to realize the breaking of the spatial symmetry of each order harmonic scattering pattern. Then, the one dimensional (1D) omnidirectional incident angle can be analytically computed using +1st and -1st order harmonics. The approach is also extended to 2D DoA estimation by using two orthogonally arranged 1D DoA modulation arrays. The accuracy of 1D DoA estimation is verified through full-wave numerical simulations. Compared to conventional DoA estimation methods, the proposed approach simplifies the computation and hardware complexity, ensuring at the same time estimation accuracy. The proposed method may have potential applications in wireless communications, target recognition, and identification.

scholarly journals Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Guimei Zheng ◽  
Jun Tang
Keyword(s):  
Electromagnetic Field ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Field Vector ◽  
Cross Product ◽  
Mimo Radar ◽  
Two Dimensional ◽  
Array Configuration ◽  
2D Doa Estimation

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

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.

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