基于TC-CNN网络的残缺TACAN空/地信号识别方法

Incomplete TACAN Air/Ground Signal Recognition Method Based on TC-CNN Network

TACAN空/地信号是一种基于脉冲调制技术的短程通信信号,主要应用于方位测量和距离测量,当使用接收器捕获TACAN空/地信号时,由于信噪比、信号完整度等差异,导致信号识别率较低。针对传统模板匹配算法无法有效对残缺的TACAN空/地信号进行识别的问题,提出了一种新型卷积神经网络(Convolutional Neural Network,CNN)模型,该模型以传统CNN模型为基础,同时加入了长短期记忆(Long Short-Term Memory,LSTM)人工神经网络结构以提高模型对信号时序特征的识别能力,实验结果表明,当数据丢失率低于30%时,该模型可以达到84%以上的识别率。

TACAN air/ground signal is a short-range communication signal based on pulse modulation technology.It is mainly used for azimuth measurement and distance measurement.When the receiver is used to capture TACAN air/ground signal,the signal recognition rate is low due to differences in signal-to-noise ratio and signal integrity.To solve the problem that traditional template matching algorithms cannot effectively identify incomplete TACAN air/ground signals,a new type of convolutional neural network model is proposed.The model is based on the traditional Convolutional Neural Network(CNN)model.The artificial neural network structure of Long Short-Term Memory(LSTM)is added to improve the model’s ability to recognize signal timing characteristics.The experimental results show that when the data loss rate is less than 30%,the recognition rate of this model can reach more than 84%.