Abstract:

An estimation and sorting method for frequency-hopping signals based on improved connected region labeling is proposed to tackle the problem that small unmanned aerial vehicles （UAVs） are difficult to identify in a complex electromagnetic environment. Firstly， the energy threshold statistical method based on local windows is used to denoise the signal in the time and frequency domains. Then， the conventional connected region labeling method is improved to design a method to associate segments of the connected region and a method to suppress interference， which solves the problems of connected region breaks and interference mixing. After that， in the case of the mixing of multi-frequency-hopping signals， the connected region is reconstructed by the time–frequency amplitude difference. Finally， parameter extraction and signal sorting and identification are carried out according to the improved label map of the connected region. The simulation shows that under the environment of noise， interference， and multi-frequency-hopping signal aliasing， the estimation accuracy of signal parameters and the correct identification probability of target signals are significantly higher than those of the conventional connected region labeling method.

Key words: frequency-hopping signal, connected region labeling, signals aliasing, time-frequency amplitude difference, parameter estimation, sorting and identification

摘要：

针对小型无人机在复杂电磁环境下难以识别的问题，提出了基于改进连通区域标记的跳频信号估计与分选方法。首先，采用基于局部窗口的能量门限统计法，在信号时频域去噪；接着，对常规连接区域标记法进行改进，设计了新的连接区域片段关联和干扰抑制方法，解决连接区域断裂和干扰混合的问题；然后，针对多跳频信号混合情况，利用时频幅度差异重建连接区域；最后，根据改进的连通区域标记图进行参数抽取和信号分选识别。仿真结果显示，在噪声、干扰和多跳频信号混叠环境下，信号参数估计精度和目标信号的正确识别概率均明显高于常规连通区域标记方法。

关键词: 跳频信号, 连通区域标记, 信号混叠, 时频幅度差异, 参数估计, 分选识别