Nonlinear System Identification Through Amplitude-Phase-Frequency Characterization

IF 5.5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-06-10 DOI:10.1109/TSP.2026.3702569

Xiaojing Huang

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引用次数: 0

Abstract

Based on expanded memory polynomial (EMP) and orthogonal EMP (OEMP), this paper addresses the nonlinear system identification (NSI) problem by introducing an amplitude-phase-frequency (APF) characterization framework to enable performance measurability, monotonicity, and optimality. APF characteristic functions (CFs) are defined to better capture the nonlinear distortions arising from both the amplitude and phase of the input signal. It is shown that an APF CF can be expressed as a weighted sum of characteristic basis functions (CBFs) or their orthogonal counterparts (OCBFs), with the associated CBF or OCBF frequency responses directly linked to the EMP or OEMP coefficients. As a result, NSI can be carried out by alternatively estimating the CBF or OCBF frequency responses. The paper also presents detailed estimation methods employing frequency modulated continuous wave (FMCW) test signals and analyzes different mean-squared-error (MSE) measurement strategies. Simulation and experimental results using a 73.5 GHz carrier frequency and 2.125 GHz bandwidth millimeter wave communication system validate the effectiveness and practical applicability of the proposed technique.

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基于幅相频特性的非线性系统辨识

基于扩展记忆多项式（EMP）和正交EMP (OEMP)，本文通过引入幅相频（APF）表征框架来解决非线性系统识别（NSI）问题，从而实现性能的可测量性、单调性和最优性。定义了APF特征函数（CFs），以便更好地捕获由输入信号的幅度和相位引起的非线性畸变。结果表明，APF CF可以表示为特征基函数（CBF）或正交基函数（OCBF）的加权和，相关的CBF或OCBF频率响应与EMP或OEMP系数直接相关。因此，NSI可以通过交替估计CBF或OCBF频率响应来进行。本文还详细介绍了调频连续波（FMCW）测试信号的估计方法，并分析了不同的均方误差（MSE）测量策略。基于73.5 GHz载波频率和2.125 GHz带宽的毫米波通信系统的仿真和实验结果验证了该技术的有效性和实用性。

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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来源期刊

IEEE Transactions on Signal Processing 工程技术-工程：电子与电气

CiteScore

11.20

自引率

9.30%

发文量

310

审稿时长

3.0 months

期刊介绍： The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.