RADIO-FREQUENCY INTERFERENCE LOCATION, DETECTION AND CLASSIFICATION USING DEEP NEURAL NETWORKS

被引：6
|
作者：
Perez, A. [1 ,2 ]
Querol, J. [3 ]
Park, H. [1 ,2 ]
Camps, A. [1 ,2 ]
机构：
[1] Univ Politecn Catalunya BarcelonaTech, CommSensLab UPC, Barcelona, Spain
[2] IEEC CTE UPC, Barcelona, Spain
[3] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Luxembourg, Luxembourg
来源：
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年
关键词：
RFI; jamming; GNSS; GNSS-R; detection; mitigation;
D O I：
10.1109/IGARSS39084.2020.9324532
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.
引用
收藏
页码：6977 / 6980
页数：4
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