基于谱图分解的宽带通信信号智能检测算法

Intelligent detection algorithm of broadband communication signal based on spectral decomposition

对于宽带通信信号检测问题,针对目前基于深度学习的信号检测算法不适应于处理大带宽和大时宽的宽带信号以及对信号时频参数估计存在的固有偏差问题,提出基于谱图分解的宽带通信信号智能检测算法,完成对大带宽接收信号中窄带信号的高效准确检测。首先将由宽带信号转化而来的灰度时频谱图通过谱图分解得到适合于目标检测网络输入大小的子谱图,然后使用改进的无锚框YOLOx目标检测算法对子谱图中的窄带信号进行检测,最后将子谱图的信号检测结果融合得到窄带信号的时频参数等检测结果。经过实验测试得出,该算法能够适应复杂的噪声环境,与其他深度学习算法和传统算法相比,具有较高的信号检测概率,较低的虚警概率,较小的信号参数估计平均误差,其检测精度更高,鲁棒性、实用性、通用性更强。

For broadband communication signal detection problem,as the current signal detection algorithm based on deep learning is not suitable for dealing with large bandwidth and large wide broadband signals,and there is the inherent deviation in signal frequency parameter estimation,we put forward intelligent broadband communication signal detection algorithm based on spectrum decomposition,thus to complete highly accurate detection of narrowband signal in large bandwidth receiving signal.First,the broadband signal is transformed into a grayscale timefrequency spectrum which is subsequently decomposed into a sub-spectrum suitable for the input size of the target detection network.Then,the anchor-free YOLOx target detection algorithm is used to detect the narrowband signal targets in the sub-spectrum.Finally,the signal detection results of the sub-spectrum are fused to obtain the detection results of the time-frequency parameters of the narrow-band signal.Experimental results show that the proposed algorithm can adapt to the complex noise environment.Compared with other deep learning algorithms and traditional energy detection algorithms,the proposed algorithm has higher signal detection accuracy,lower false alarm probability,smaller average error of signal parameter estimation,and stronger robustness,practicability and versatility.