Calibration of antenna pattern and phase errors of a cross‐loop/monopole antenna array in high‐frequency surface wave radars

Zhou, Hao; Wen, Biyang

doi:10.1049/iet-rsn.2013.0141

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Although the above types of estimation methods have good DOA estimation performances, they depend on the ideal array manifold, while in practical application scenarios, array manifolds are often affected by unknown gain-phase errors. Without array manifold calibration, the performance of DOA estimation may be greatly degraded [22]. Therefore, the study of DOA estimation methods with array gain-phase errors has theoretical significance and practical value.…”

Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

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To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.

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Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

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“…However, to cover all interested angles, they require a large amount of ship echoes, which usually take several days or longer to record. Note that the sea echoes already contain signals from all directions, so some antenna pattern estimation/calibration methods using sea echoes have been proposed [25][26][27][28]. As a kind of passive calibration, these methods employ different iterative algorithms and cost functions to calibrate the APD.…”

Section: Introductionmentioning

confidence: 99%

Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter

Li,

Liu,

Yang

et al. 2023

Remote Sensing

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The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method.

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“…In STAP, the jamming power that injects into the tracking loop is minimised to the noise level so that it no longer affects the performance of receiver, and a typical N elements M taps array can suppress NM ‐1 jamming signals. However, although STAP is very effective in anti‐jamming application, the unwanted carrier phase bias would be induced because of several factors when applying STAP, including the characteristics of antennas [4], the mismatch of front‐end channels [5], the space‐time filtering [6], and so on. It has been analysed that the positioning error that caused by the bias of STAP can be several metres, and the carrier phase positioning would be invalid as well, which may significantly degrade the performance of navigation signals [7].…”

Section: Introductionmentioning

confidence: 99%

Carrier phase correction for GNSS space‐time array processing based on frequency jerk detection

Xie

Chen

et al. 2021

IET Radar Sonar & Navi

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The signals of Global Navigation Satellite System (GNSS) are so vulnerable that can be easily jammed by intentional or unintentional interferences. One of the effective ways to protect GNSS signals from jamming is to use the Space-Time Array Processing (STAP) technology. However, undesired carrier phase bias would be induced to the signals because of the STAP, which significantly influences the precision of positioning. Many algorithms have been proposed to deal with this carrier phase bias, but most of them need the directional information of signals or the pretest of antennas. In this paper, a carrier phase correction algorithm without the need of prior information is proposed. Alarmed by the jerk of carrier frequency, the algorithm can detect the change of jamming and calculate the induced carrier phase bias. Then it can correct this bias from the signal and alleviate its influence. The simulation results show that the algorithm can limit the bias from more than 60°to less than 10°, which verifies that the proposed algorithm is effective in the low dynamic receiver. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Calibration of antenna pattern and phase errors of a cross‐loop/monopole antenna array in high‐frequency surface wave radars

Cited by 10 publications

References 25 publications

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter

Carrier phase correction for GNSS space‐time array processing based on frequency jerk detection

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