TY - GEN
T1 - Single-satellite Multi-beam Antenna Interference Localization based on Two-stage Localization Optimization
AU - Xu, Dongyu
AU - Wang, Zhaodi
AU - Leng, Biao
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/5/28
Y1 - 2025/5/28
N2 - In modern satellite communication systems, frequent interference events pose a significant threat to communication quality and service stability, especially in the context of limited orbital and frequency resources. Therefore, researching efficient interference source localization techniques is of paramount importance. This paper focuses on a single-satellite interference localization method based on onboard multi-beam antennas, proposing a two-stage localization optimization strategy to improve the accuracy and robustness of interference source localization. First, this paper validates the interference source communication link model under complex multi-factor scenarios, including interference source location, frequency, and gain measurement errors, providing reliable data support for model optimization. Next, a two-stage localization optimization method is proposed, consisting of interference evaluation and fine localization. In the first stage, the particle swarm algorithm is used for preliminary localization of the interference source, and the interference difficulty evaluation network is applied to evaluate the difficulty of interference source localization. In the second stage, the particle swarm algorithm's results are retained for easy interference sources. And an encoder-decoder-based localization model is devised to locate difficult interference sources, significantly improving localization accuracy. Experimental results show that the proposed method demonstrates strong adaptability and accuracy in various complex environments, effectively reducing interference source localization errors.
AB - In modern satellite communication systems, frequent interference events pose a significant threat to communication quality and service stability, especially in the context of limited orbital and frequency resources. Therefore, researching efficient interference source localization techniques is of paramount importance. This paper focuses on a single-satellite interference localization method based on onboard multi-beam antennas, proposing a two-stage localization optimization strategy to improve the accuracy and robustness of interference source localization. First, this paper validates the interference source communication link model under complex multi-factor scenarios, including interference source location, frequency, and gain measurement errors, providing reliable data support for model optimization. Next, a two-stage localization optimization method is proposed, consisting of interference evaluation and fine localization. In the first stage, the particle swarm algorithm is used for preliminary localization of the interference source, and the interference difficulty evaluation network is applied to evaluate the difficulty of interference source localization. In the second stage, the particle swarm algorithm's results are retained for easy interference sources. And an encoder-decoder-based localization model is devised to locate difficult interference sources, significantly improving localization accuracy. Experimental results show that the proposed method demonstrates strong adaptability and accuracy in various complex environments, effectively reducing interference source localization errors.
KW - Interference source localization
KW - Multi-beam antenna
KW - Particle swarm
KW - Satellite communication
KW - Two-stage localization optimization
UR - https://www.scopus.com/pages/publications/105007993594
U2 - 10.1145/3728199.3728261
DO - 10.1145/3728199.3728261
M3 - 会议稿件
AN - SCOPUS:105007993594
T3 - Proceedings of 2025 3rd International Conference on Communication Networks and Machine Learning, CNML 2025
SP - 374
EP - 380
BT - Proceedings of 2025 3rd International Conference on Communication Networks and Machine Learning, CNML 2025
PB - Association for Computing Machinery, Inc
T2 - 2025 3rd International Conference on Communication Networks and Machine Learning, CNML 2025
Y2 - 21 February 2025 through 23 February 2025
ER -