卫星导航接收机射频前端非线性失真建模综述

doi:10.12305/j.issn.1001-506X.2025.03.26

摘要/Abstract

摘要：

卫星导航接收机射频(radio frequency, RF)前端的非线性效应威胁导航设备的正常工作, 是卫星导航接收机抗干扰性能进一步提升的瓶颈。建立设备非线性效应的行为模型是解决非线性问题的重要方法。随着卫星导航接收机种类多样化、干扰环境复杂化, 已有的非线性模型显现出对卫星导航接收机针对性研究不足、干扰环境下可靠性不足和建模方法不适配的问题。因此, 综述主流的非线性效应行为级建模方法, 评述各建模方法的优点与不足, 介绍机器学习技术在非线性行为级建模的应用。最后, 根据对已有建模技术特点的总结与面临的问题, 对未来卫星导航接收机非线性建模技术的发展做出展望。

关键词: 卫星导航接收机, 非线性失真, 行为模型, 机器学习

Abstract:

The nonlinear effect of radio frequency (RF) front end of satellite navigation receiver threatens the normal operation of navigation equipment, which is the bottleneck to further improve the anti-interference performance of satellite navigation receiver. It is an important method to establish the behavior model of nonlinear effect of equipment. With the variety of satellite navigation receivers and the complexity of interference environment, the existing nonlinear models are shown the problems of insufficient targeted research on satellite navigation receivers, insufficient reliability under interference environment, and unsuitable modeling methods. Therefore, this paper summarizes the mainstream modeling methods of nonlinear effect behavior level, reviews the advantages and disadvantages of each modeling method, and introduces the application of machine learning technology in nonlinear behavior level modeling. Finally, based on the summary of the characteristics of existing modeling technologies and the problems faced, the future development of nonlinear behavior modeling technology of satellite navigation receiver is prospected.

Key words: satellite navigation receiver, nonlinear distortion, behavior model, machine learning

中图分类号:

TN965.5

刘嘉俊, 鲁祖坤, 肖伟, 李宗楠, 刘哲. 卫星导航接收机射频前端非线性失真建模综述[J]. 系统工程与电子技术, 2025, 47(3): 938-950.

Jiajun LIU, Zukun LU, Wei XIAO, Zongnan LI, Zhe LIU. Review of nonlinear distortion modeling in RF front end of satellite navigation receivers[J]. Systems Engineering and Electronics, 2025, 47(3): 938-950.

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模型	阶数	记忆深度	NMSE/dB	系数数量
MP	5	3	-30.59	20
GMP	5	3	-30.93	52
DDRMP	5	3	-31.61	17
CPWL	5	3	-33.12	22

模型	优点	缺点
Volterra级数	通用性好, 可使用LS法直接提取模型参数	收敛速度慢, 只适合对弱非线性系统, 复杂度高
MP	复杂度极低	难以精确建模复杂非线性系统
GMP	复杂度较低, 建模精度优于MP模型	非线性阶数和记忆长度确定困难
DDR	模型复杂度低于GMP模型, 可以准确描述静态强非线性效应	模型精度低于GMP模型
Hammerstein	原理简单, 模型复杂度低	串联结构导致模型精度不足
CPWL	可对强非线性系统建模, 灵活高效, 结构简单, 参数提取便捷	需要阈值分配算法辅助建模

参数提取算法	优点	缺点
LS	收敛速度快、精度高	易出现病态问题与过拟合问题、难以处理记忆效应模型
直方图优化方法	所需训练样本数量少、精度高	对非线性特性变化敏感, 难以适应统计量变化大的信号, 计算复杂度较高
岭回归算法	可以进行小样本训练而不产生病态问题	难以确认合适的岭参数以平衡复杂度与精度
主成分分析	可以显著降低模型复杂度	需要权衡精度与信号的缩减程度
偏LS法	可以在不损失精度的情况下减少估计参数的数量	需要获得先验信息进一步提升精度

模型	代表模型	优点	缺点
DNN	RVTDNN	适应性强、拟合能力强	存在直流偏移、I/Q不平衡问题
RNN	LSTM	可以有效提取长时记忆效应、泛化能力强、精度高	对信号的同相和正交部分分别进行运算, 不符合放大器的物理机制
灰箱模型	MP-NN	训练速度快、精度高	需要先验信息
SVM	SVR	泛化能力强	学习速度低、复杂度较高

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