排列不变的多重叠干扰信号时频分割与参数估计
Permutation-Invariant Time-Frequency Segmentation and Parameter Estimation of Multiple Overlapping Interference Signals
浏览论文内容
中文总结 AI 辅助
提出PI-SCAN框架，基于集合学习实现重叠干扰信号的排列不变分割与参数估计，在工业场景中达到74.8%检测F1分数和97.3%类型准确率。
中文摘要 AI 辅助
射频干扰威胁着卫星导航、无线通信、雷达和工业传感。因此，可靠运行需要频谱态势感知来检测干扰并确定活跃源的数量、时频占用和特征。在现实环境中，多个源可能重叠。因此，特定源的分割和表征尤其具有挑战性。在本文中，我们提出了PI-SCAN（排列不变分割、表征和分配网络），一种用于重叠干扰信号的特定源分割和表征的联合基于集合的学习框架。从复数IQ样本导出的对数功率谱图被映射到六个无序预测槽。对于每个槽，模型估计目标性、干扰类型、中心频率、带宽、接收信号强度、二进制偏移载波（BOC）调制和特定源分割掩码。排列不变的匈牙利分配将预测槽与无序的真实源对齐，而加权多任务目标联合优化各个预测任务。该框架在包含一到六个同时活跃干扰源混合物的射线追踪工业环境中进行评估，这些干扰源来自六个干扰类别中的94种波形配置。完整的七任务配置实现了74.8%的检测F1分数，类型和BOC准确率分别为97.3%和98.4%，中心频率、带宽和信号强度的平均绝对误差分别为1.29MHz、2.54MHz和3.19dB。仅使用目标性、类型和掩码预测可将F1分数提高到91.1%，突显了检测与全面表征之间的权衡。
英文摘要
Radio-frequency interference threatens satellite navigation, wireless communications, radar, and industrial sensing. Reliable operation therefore requires spectrum situational awareness to detect interference and determine the number, time-frequency occupancy, and characteristics of active sources. In realistic environments, multiple sources may overlap. Source-specific segmentation and characterization are therefore particularly challenging. In this paper, we propose PI-SCAN (Permutation-Invariant Segmentation, Characterization, and Assignment Network), a joint set-based learning framework for source-specific segmentation and characterization of overlapping interference signals. A logarithmic power spectrogram derived from complex IQ samples is mapped to six unordered prediction slots. For each slot, the model estimates objectness, interference type, center frequency, bandwidth, received signal strength, binary offset carrier (BOC) modulation, and a source-specific segmentation mask. Permutation-invariant Hungarian assignment aligns the predicted slots with the unordered ground-truth sources, while a weighted multi-task objective jointly optimizes the individual prediction tasks. The framework is evaluated using a ray-traced industrial environment containing mixtures of one to six simultaneously active interferers drawn from 94 waveform configurations across six interference classes. The full seven-task configuration achieves a detection F1-score of 74.8%, type and BOC accuracies of 97.3% and 98.4%, and mean absolute errors of 1.29MHz, 2.54MHz, and 3.19dB for center frequency, bandwidth, and signal strength. Using only objectness, type, and mask prediction increases the F1-score to 91.1%, highlighting the trade-off between detection and comprehensive characterization.
发表机构
- Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）
- University of Technology Nürnberg (UTN)（纽伦堡工业大学）
机构由 AI 辅助整理，请以论文原文为准。
