scholarly journals Quantifying and Reducing the DOA Estimation Error Resulting from Antenna Pattern Deviation for Direction-Finding HF Radar

2017 ◽  
Vol 9 (12) ◽  
pp. 1285 ◽  
Author(s):  
Yeping Lai ◽  
Hao Zhou ◽  
Yuming Zeng ◽  
Biyang Wen
Keyword(s):  
Estimation Error ◽  
Doa Estimation ◽  
Direction Finding ◽  
Antenna Pattern ◽  
Hf Radar ◽  
Pattern Deviation

Measuring Antenna Patterns for Ocean Surface Current HF Radars with Ships of Opportunity

2014 ◽  
Vol 31 (7) ◽  
pp. 1564-1582 ◽  
Author(s):  
Brian M. Emery ◽  
Libe Washburn ◽  
Chad Whelan ◽  
Don Barrick ◽  
Jack Harlan
Keyword(s):  
Surface Current ◽  
Ocean Surface ◽  
Radar Data ◽  
Direction Finding ◽  
Antenna Pattern ◽  
Hf Radar ◽  
Ocean Current ◽  
Identification System ◽  
Screening Methods ◽  
Ocean Surface Current

Abstract HF radars measure ocean surface currents near coastlines with a spatial and temporal resolution that remains unmatched by other approaches. Most HF radars employ direction-finding techniques, which obtain the most accurate ocean surface current data when using measured, rather than idealized, antenna patterns. Simplifying and automating the antenna pattern measurement (APM) process would improve the utility of HF radar data, since idealized patterns are widely used. A method is presented for obtaining antenna pattern measurements for direction-finding HF radars from ships of opportunity. Positions obtained from the Automatic Identification System (AIS) are used to identify signals backscattered from ships in ocean current radar data. These signals and ship position data are then combined to determine the HF radar APM. Data screening methods are developed and shown to produce APMs with low error when compared with APMs obtained with shipboard transponder-based approaches. The analysis indicates that APMs can be reproduced when the signal-to-noise ratio (SNR) of the backscattered signal is greater than 11 dB. Large angular sectors of the APM can be obtained on time scales of days, with as few as 50 ships.

scholarly journals DOA Estimation Performance Analysis and Verification of Dual Crossed-Loop/Monopole Antenna Array

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Sijie Wang ◽  
Biyang Wen ◽  
Yingwei Tian
Keyword(s):  
Antenna Array ◽  
Estimation Error ◽  
Doa Estimation ◽  
Hf Radar ◽  
Estimation Accuracy ◽  
Dominant Factor ◽  
Application Range ◽  
Estimation Performance ◽  
Wave Radar ◽  
Target Monitoring

The compact high-frequency surface wave radar using a crossed-loop/monopole (CLM) antenna as the receiving sensor has been widely used in ocean remote sensing and target monitoring. However, the direction of arrival (DOA) estimation accuracy of a single CLM antenna is the dominant factor that restricts the target monitoring performance of the compact HF radar. Besides, the single CLM antenna can estimate two signals simultaneously at most, but its effectiveness is challenged by the pattern distortion and the existence of coherent sources, which limits the application range of the compact HF radar. In this study, a compact array combining two CLM antennas is proposed to improve the DOA estimation accuracy and solve the multisource DOA estimation problem. The estimation error and multisource DOA estimation performance of a dual CLM antenna array are analyzed by formula derivation and simulation. Furthermore, the field experiment results are given to demonstrate the performance improvement of the dual CLM antenna 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 An Effective Technique for Enhancing Direction Finding Performance of Virtual Arrays

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Wenxing Li ◽  
Xiaojun Mao ◽  
Wenhua Yu ◽  
Chongyi Yue
Keyword(s):  
Antenna Array ◽  
Interpolation Method ◽  
Doa Estimation ◽  
Direction Finding ◽  
Estimation Accuracy ◽  
Signal Subspace ◽  
Effective Technique ◽  
Resolution Capability ◽  
Significant Bias ◽  
Virtual Array

The array interpolation technology that is used to establish a virtual array from a real antenna array is widely used in direction finding. The traditional interpolation transformation technology causes significant bias in the directional-of-arrival (DOA) estimation due to its transform errors. In this paper, we proposed a modified interpolation method that significantly reduces bias in the DOA estimation of a virtual antenna array and improves the resolution capability. Using the projection concept, this paper projects the transformation matrix into the real array data covariance matrix; the operation not only enhances the signal subspace but also improves the orthogonality between the signal and noise subspace. Numerical results demonstrate the effectiveness of the proposed method. The proposed method can achieve better DOA estimation accuracy of virtual arrays and has a high resolution performance compared to the traditional interpolation method.

scholarly journals Covariance Matrix Reconstruction for Direction Finding with Nested Arrays Using Iterative Reweighted Nuclear Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Weijie Tan ◽  
Xi’an Feng
Keyword(s):  
Covariance Matrix ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Finding ◽  
Nuclear Norm ◽  
Low Rank ◽  
Matrix Reconstruction ◽  
Linear Transform

In this paper, we address the direction finding problem in the background of unknown nonuniform noise with nested array. A novel gridless direction finding method is proposed via the low-rank covariance matrix approximation, which is based on a reweighted nuclear norm optimization. In the proposed method, we first eliminate the noise variance variable by linear transform and utilize the covariance fitting criteria to determine the regularization parameter for insuring robustness. And then we reconstruct the low-rank covariance matrix by iteratively reweighted nuclear norm optimization that imposes the nonconvex penalty. Finally, we exploit the search-free DoA estimation method to perform the parameter estimation. Numerical simulations are carried out to verify the effectiveness of the proposed method. Moreover, results indicate that the proposed method has more accurate DoA estimation in the nonuniform noise and off-grid cases compared with the state-of-the-art DoA estimation algorithm.

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