Kalman Tracking of GEO Satellite Signal for Opportunistic Rain Rate Estimation

F. Giannetti, R. Reggiannini, M. Moretti, S. Scarfone, A. Colicelli, F. Caparrini, G. Bacci, A. Petrolino, A. Vaccaro, E. Adirosi, A. Mazza, L. Facheris
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引用次数: 1

Abstract

Ahstract- In the NEFOCAST project we aim at estimating rainfall by the opportunistic use of the signal attenuation due to the propagation channel in satellite communications. The estimation is performed by reverse engineering the effects of the various propagation phenomena on the satellite signal. However, the accuracy of the estimation is affected by several factors: in first place the rapid fluctuations in signal amplitude caused by small-scale irregularities in the tropospheric refractive index; secondly, the perturbations of the orbit of GEO satellites, such as the gravitational effects of the moon and the sun, which, even if periodically counteracted by correction maneuvers, nevertheless cause residual orbit inclinations. The problem with all these factors is that they can cause large deviations in the clear-sky measurements that can be misinterpreted as rain events. In this paper we address these problems by employing two Kalman filters designed to track slow and fast changes of the received signal energy, so that the rain events can be reliably estimated.
基于卡尔曼跟踪的地球同步轨道卫星信号机会雨率估计
摘要:在NEFOCAST项目中,我们的目标是通过利用卫星通信中传播信道引起的信号衰减来估计降雨量。通过逆向工程的方法估计了各种传播现象对卫星信号的影响。然而,估计的准确性受到几个因素的影响:首先是对流层折射率的小尺度不规则引起的信号幅度的快速波动;第二,地球同步轨道卫星轨道的扰动,如月球和太阳的引力效应,即使定期通过修正机动抵消,仍然会引起残余的轨道倾角。所有这些因素的问题在于,它们会导致晴空测量结果出现较大偏差,从而可能被误解为降雨事件。在本文中,我们采用两个卡尔曼滤波器来跟踪接收信号能量的慢速和快速变化来解决这些问题,从而可靠地估计降雨事件。
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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