Cross Coupling Mitigation in Polarimetric PAR via Antenna Tilt

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN
I. Ivić
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引用次数: 0

Abstract

The existence of significant cross-polar antenna patterns, as well as the scan-dependent measurement biases, inherent to the polarimetric phased array radar (PPAR), are among the most important risk factors for using this technology in weather observations. The cross-polar patterns on receive induce cross coupling between returns from the two orthogonal fields causing biases in polarimetric variable estimates. Furthermore, the electromagnetic coupling in hardware may exacerbate the cross-coupling effects. To address this problem, a pulse-to-pulse phase coding in either the horizontal or vertical ports of the transmission elements has been proposed. However, it does not affect the scan-dependent system biases in PPAR estimates which require corrections via calibration mechanisms. Further, the cross-coupling signals are proportional to the cross-polar pattern power levels, rendering mitigation effective only at steering angles where these levels are sufficiently low (e.g., approximately less than ~-25 dB). In that regard, any approach that augments the number of such steering angles benefits the cross-coupling mitigation effectiveness. Herein, a simple approach that has a potential to achieve this via antenna tilt is presented.
通过天线倾斜抑制极化标准杆数中的交叉耦合
极化相控阵雷达(PPAR)固有的显著跨极天线方向图的存在,以及扫描相关的测量偏差,是在天气观测中使用该技术的最重要风险因素之一。接收上的交叉极化模式导致来自两个正交场的回波之间的交叉耦合,从而导致极化变量估计中的偏差。此外,硬件中的电磁耦合可能会加剧交叉耦合效应。为了解决这个问题,已经提出了在传输元件的水平或垂直端口中的脉冲到脉冲相位编码。然而,它不会影响PPAR估计中的扫描相关系统偏差,PPAR估计需要通过校准机制进行校正。此外,交叉耦合信号与交叉极性模式功率电平成比例,使得仅在这些电平足够低(例如,大约小于~25dB)的转向角处缓解才有效。在这方面,任何增加这种转向角数量的方法都有利于交叉耦合缓解的有效性。在此,提出了一种简单的方法,该方法有可能通过天线倾斜来实现这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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