基于峰值多层级支持向量机的X射线通信解调技术研究
作者:
作者单位:

1.中国科学院微小卫星创新研究院 上海 201306;2.南京航空航天大学 南京 210016

作者简介:

李诗嘉 1995年生,硕士。
高有涛 1983年生,博士,副教授。

中图分类号:

V1;TN91

基金项目:

中央高校基本科研业务费专项资金资助(NS2024054)


X-ray Communication Demodulation Technology Based on Peak Multilevel Support Vector Machine
Author:
Affiliation:

1.Innovation Academy for Microsatellites of CAS, Shanghai, 201306;2.Nanjing University of Aeronautics and Astronautics, Nanjing, 210016

  • 摘要
  • | | | | |
  • 文章评论
    摘要:

    X射线通信技术是一种以X射线为载波的空间通信方式,它具有通信带宽大、重量轻、体积小、功耗低、保密性高等优势。通过设计多靶材X射线信号调制装置可以提高X射线的通信速率,在信号接收端要保证X射线能谱的识别准确率,才能真正发挥基于能量负载的X射线通信的优势。本文针对多靶材X射线特征能谱精确识别问题,提出基于峰值多层级支持向量机的X射线通信解调方法,设计了适合四码元通信的峰值多层级支持向量机分类器,通过参数调优和验证确保了高准确率和泛化能力。仿真结果证明:支持向量机为基于能量负载的X射线通信方法提供了高效、准确、鲁棒的信号识别解决方案。

    Abstract:

    X-ray communication technology is a kind of space communication mode using X-ray as a carrier, which has the advantages of a large communication bandwidth, light weight, small volume, low power consumption, and high confidentiality. By designing a multi-target X-ray signal modulation device, the X-ray communication rate can be improved, and the accuracy of X-ray energy spectrum recognition at the signal receiving end can be ensured, so that the advantages of X-ray communication based on energy load can be truly played. In this paper, an X-ray communication demodulation method based on peaking multi-level support vector machine is proposed to accurately identify the X-ray characteristic energy spectrum of multi-target materials. A peaking multi-level support vector machine classifier suitable for four-element communication is designed. The parameter tuning and verification ensure high accuracy and generalization ability. The simulation results show that support vector machine provides an efficient, accurate, and robust signal recognition solution for X-ray communication based on energy load.

    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

李诗嘉,高有涛.基于峰值多层级支持向量机的X射线通信解调技术研究[J].遥测遥控,2024,45(5):38-49.

复制
分享
文章指标
  • 点击次数:49
  • 下载次数: 82
  • HTML阅读次数: 9
  • 引用次数: 0
    参考文献
    [1] HANEL R, CONRATH B, FLASAR F M, et al. Infrared observations of the Saturnian system from Voyager1[J]. Science, 1981, 212(4491): 192-200.
    [2] 黑大千, 金利民, 贾文宝, 等. 一种空间X射线通信中信号的调制解调装置及方法: 201810193984.8[P]. 2020-10-23.
    [3] MAEO S, SAKAI I, KUZUSHITA K, et al. Development of micro X-ray fluorescence spectrometer with multi excitation sources[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2007, 62(6-7): 562-566.
    [4] MAEO S, KRÄMER M, UTAKA T, et al. Development of a micro-focus X-ray fluorescence spectrometer using multiple target anode monochromatic X-ray sources[J]. X-Ray Spectrometry: An International Journal, 2009, 38(4): 333-337.
    [5] ZHAO Lei, JIA Wenbao, JIN Limin, et al. A design of transmission-type multi-target X-ray tube based on electric field modulation[J]. Nuclear Engineering and Technology, 2021, 53(9): 3026-3034.
    [6] JIN Limin, JIA Wenbao, Daqian HEI, et al. Development of an X-ray tube with two selective targets modulated by a magnetic field[J]. Review of Scientific Instruments, 2019, 90(8): 083105.
    [7] JIN Limin, JIA Wenbao, Daqian HEI, et al. Feasibility study of the high frequency X-ray communication using selective characteristic X-rays[J]. Optics Communications, 2021, 484: 126697.
    [8] JIN Limin, JIA Wenbao, Daqian HEI, et al. Design and analysis of a multi-carrier X-ray communication system[J]. Optics Communications, 2022, 524: 128769.
    [9] DANIEL G, CERAUDO F, LIMOUSIN O, et al. Automatic and real-time identification of radionuclides in gamma-ray spectra: A new method based on convolutional neural network trained with synthetic data set[J]. IEEE Transactions on Nuclear Science, 67(4): 644-653.
    [10] YANG H, HARE J, KARGALTSEV O, et al. Classify-ing unidentified X-ray sources in the Chandra source catalog using a multiwavelength machine-learning approach[J]. The Astrophysical Journal, 2022, 941(2): 13656.
    [11] XIE Mingliang, CHEN Yuqing, YU Lei, et al. Technical research on rapid source term calculation and prediction technology of nuclear accidents based on Bayesian network[J]. Nuclear Engineering and Technology, 2023(61): 1061-1074.
    [12] 管弦, 魏星, 李子锟, 等. 基于SVM的放射源快速定位技术研究[J]. 核技术, 2023, 46(9): 17-27.GUAN Xuan, WEI Xing, LI Zikun, et al. Fast localization of radiation sources based on Support Vector Machine[J]. Nuclear Techniques, 2023, 46(9): 17-27.
    [13] 王乃芯. 多分类支持向量机的研究[D]. 上海: 华东师范大学, 2020.
历史
  • 收稿日期:2024-05-20
  • 最后修改日期:2024-07-09
  • 在线发布日期: 2024-09-26