Portable high-frequency radar antenna pattern calibration and multiple data quality control methods

doi:10.3969/j.issn.1000-5641.2025.06.014

Abstract

Abstract:

Given their wide coverage, high resolution, and all-weather observational capabilities, high-frequency radars hold considerable potential for ocean current observation and forecasting. In this study, we address two key steps in the radar data processing chain, namely, antenna pattern calibration using an unmanned aerial vehicle (UAV) and anomaly detection using multiple algorithms. A UAV equipped with a beacon–global positioning system was employed for radar antenna pattern calibration. Experiments were conducted at the Jinshan–Luchaogang radar station in Shanghai, China. Following calibration, the root-mean-square errors of current direction and speed decreased by 17.76° and 0.04 m/s, respectively, with corresponding improvements in the correlation coefficients of 0.11 and 0.23. Furthermore, quality control of the radar-derived current data from the Jinshan–Luchaogang area was implemented using three methods: the standard deviation threshold method, boxplot analysis, and Isolation Forest algorithm. All three methods effectively identified anomalies in the marginal and nearshore regions of the observed current fields. Among them, the Isolation Forest algorithm exhibited superior spatiotemporal sensitivity and a broader detection range. These results indicate that combining improvements in the high-frequency radar data quality control with UAV-based antenna calibration can substantially enhance data quality and improve the reliability of high-frequency radar-derived data for ocean dynamic monitoring. Based on these findings, future research is recommended to incorporate multi-source observations and physical constraints to further improve high-frequency radar data quality control and calibration.

Key words: high-frequency radar, antenna pattern calibration, data quality control method, surface current

CLC Number:

TN958.95

Xuyun WU, Pixue LI, Yuqi WANG. Portable high-frequency radar antenna pattern calibration and multiple data quality control methods[J]. J* E* C* N* U* N* S*, 2025, 2025(6): 128-140.

Figures/Tables 9

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参数名	取值	含义及设定依据
树数量	100	100 棵被证明可兼顾效率与稳定性
子样本大小$ \boldsymbol{\psi } $	/	取样本总数与 256 的最小值, 每棵树的最大深度$ h_{\mathrm{m}\mathrm{a}\mathrm{x}}=\lceil\mathrm{l}\mathrm{o}\mathrm{g}_2\left(\mathit{ \boldsymbol{\psi } }\right)\rceil $
异常点比例	/	算法根据训练数据自动推断
特征子集比例	1.0	使用全部特征
随机种子	None	不固定种子