宽带时域智能测试技术研究进展及未来展望

Research progress and future perspectives of intelligent broadband time-domain measurement technologies

摘要

在核爆轰试验、电子对抗、6G 通信测试和芯片测试等领域，超宽带复杂信号的精确测试是评估系统性能、反演物理过程和发现故障目标的重要基础，也是宽带时域测试仪器核心能力的重要体现。然而，随着电子信号向超宽带、大动态、短时突发和强随机方向发展，传统测试技术受限于固定采样架构、有限实时处理能力以及单域分析模式，面临以下 3 个主要挑战：1）宽带稀疏信号测试受带宽与分辨率相互制约，难以兼顾高带宽与高精度；2）极高速数据流中偶发异常易被海量正常数据淹没，难以实现实时捕获；3）复杂信号特征分布于时域、频域、调制域等信息空间，传统单域分析难以实现多域信息同步测试。针对上述问题，该文提出时频幅混合交织可重构采样架构，研究目标特征驱动的智能采样、偶发异常智能捕获和多域智能分析方法。通过自主学习信号多域特征分布，动态优化采样资源、检测资源和分析策略，实现超宽带复杂信号高精度采样、偶发异常无遗漏捕获和多域智能分析。研究推动宽带时域测试仪器由固定配置、被动采集向自适应采集、自主感知和智能分析演进，为新一代智能测试仪器发展提供理论与技术支撑。

Abstract

In nuclear explosion tests, electronic warfare, 6G communication testing, and chip testing, the accurate measurement of ultra-wideband (UWB) complex signals is fundamental for system performance evaluation, physical process reconstruction, and fault target identification, and represents a core capability of broadband time-domain test instruments. However, as electronic signals evolve toward ultra-wide bandwidth, large dynamic range, short-duration bursts, and strong randomness, conventional measurement techniques are constrained by fixed sampling architectures, limited real-time processing capability, and single-domain analysis, leading to three major challenges: 1) broadband sparse signal measurement suffers from the trade-off between bandwidth and resolution, making it difficult to achieve both wide bandwidth and high accuracy; 2) sporadic anomalies in ultra-high-speed data streams are easily overwhelmed by massive normal data, preventing reliable real-time capture; and 3) signal characteristics are distributed across multiple information domains, including the time, frequency, and modulation domains, making conventional single-domain analysis difficultly to achieve synchronized multi-domain measurement. To address these challenges, this paper proposes a reconfigurable hybrid interleaved sampling architecture in the time, frequency, and amplitude domains, together with target feature-driven intelligent sampling, anomaly capture, and multi-domain analysis methods. By autonomously learning the multi-domain feature distribution of signals, the proposed approach dynamically optimizes sampling resources, detection resources, and analysis strategies, enabling high-precision acquisition, lossless capture of sporadic anomalies, and intelligent multi-domain analysis of UWB complex signals. This work advances time-domain test instruments from fixed-configuration passive acquisition toward adaptive acquisition, autonomous perception, and intelligent analysis, providing theoretical foundations and technical support for next-generation intelligent test instruments.

关键词

宽带时域测试 / 高精度采样 / 异常捕获 / 智能多域分析

Key words

broadband time-domain measurement / high-precision sampling / transient anomalies capture / intelligent multidomain analysis

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基金资助

国家自然科学基金(62371097, 62201125)