Analysis of Direction-of-Arrival Estimation for a Floating High-Frequency Radar With Yaw Rotation

IF 3.8 2区 工程技术 Q1 ENGINEERING, CIVIL
Xianzhou Yi;Xiongbin Wu;Bin Wan;Zhihui Li
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Abstract

Mounting a high-frequency radar on a floating platform can increase flexibility compared to a shore-based high-frequency radar. However, the direction-of-arrival (DOA) estimation is significantly affected by yaw rotation. To analyze the DOA estimation results and optimize the adaptive beamforming methods for yaw compensation, two parameters are introduced: the beam shape keeping factor (BSKF) and the gain of noise power (GNP). The BSKF represents the integration of steering vector errors in the beam domain, while the GNP is the 2-norm ratio between the optimal and reference weight vectors. A smaller BSKF tends to have a reduced DOA estimation bias, and a lower GNP indicates a higher signal-to-noise ratio (SNR). Thus, BSKF and GNP are used to separately evaluate the bias and the stability of the DOA estimation. To avoid the SNR loss caused by adaptive beamforming, a comprehensive adaptive beamforming method is proposed, which balances BSKF and GNP. The effectiveness of these two parameters is confirmed through simulations and field experiments. Results show that an adaptive beamforming method for yaw compensation should minimize both BSKF and GNP.
具有偏航旋转功能的浮动高频雷达的到达方向估计分析
与岸基高频雷达相比,在浮动平台上安装高频雷达可以提高灵活性。然而,到达方向(DOA)估计受偏航旋转的影响很大。为了分析 DOA 估计结果并优化偏航补偿自适应波束成形方法,引入了两个参数:波束形状保持因子(BSKF)和噪声功率增益(GNP)。BSKF 表示波束域中转向矢量误差的积分,而 GNP 则是最佳权重矢量与参考权重矢量之间的 2 正比。BSKF 越小,DOA 估计偏差越小;GNP 越小,信噪比(SNR)越高。因此,BSKF 和 GNP 分别用于评估 DOA 估计的偏差和稳定性。为了避免自适应波束成形造成的信噪比损失,提出了一种平衡 BSKF 和 GNP 的综合自适应波束成形方法。通过模拟和现场实验证实了这两个参数的有效性。结果表明,用于偏航补偿的自适应波束成形方法应同时最小化 BSKF 和 GNP。
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来源期刊
IEEE Journal of Oceanic Engineering
IEEE Journal of Oceanic Engineering 工程技术-工程:大洋
CiteScore
9.60
自引率
12.20%
发文量
86
审稿时长
12 months
期刊介绍: The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.
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