基于HOG的跳频信号自动化检测模型

Automatic Detection Model for HOG-based Frequency Hopping Signal

针对非合作通信场景下跳频信号自动化检测识别问题,在时频分析的基础上,提出了一种基于方向梯度直方图的跳频信号自动化检测模型。该模型通过时频分析方法将无线通信信号转化为时频瀑布图,采用方向梯度直方图特征表征跳频信号在时频瀑布图上呈现的独特结构特征。然后,采用基于adaboost的算法实现跳频信号的自动化检测,并且在室内多径信号环境下进行了测试验证。结果表明,在20 dB信噪比条件下,检测正确率达到了97.51%。

Aiming at the problem of automatic detection and identification of frequency hopping signals in non-cooperative communication scenarios,based on time-frequency analysis,an automatic detection model of frequency hopping signals on the basis of directional gradient histogram is proposed.The time-frequency analysis method is used to transform the wireless communication signal into a time-frequency waterfall graph.The directional gradient histogram feature is used to characterize the unique structural features of the frequency-hopping signal presented on the time-frequency waterfall graph.Then,an adaptive algorithm based on adaboost is used to realize the automatic detection of the frequency hopping signal,and the test is performed in an indoor multipath signal environment.The results indicate that the detection accuracy reaches 97.51%under 20 dB signal-to-noise ratio.