Gu Han-Qing, Liu Xia-Xia, Xu Lu, Zhang Yi-Jia, Lu Zhe-Ming
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2023 Jul 26;23(15):6691. doi: 10.3390/s23156691.
With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.
随着直接序列扩频（DSSS）信号的广泛应用，DSSS通信系统的综合性能不断提升，使得通信对抗中的电子侦察链路愈发困难。电子侦察技术作为现代电子战的基本手段，主要包括信号检测、识别和参数估计。目前，对DSSS检测算法的研究大多基于DSSS信号的相关特性，自相关算法是实际工程中最成熟且应用最广泛的方法。随着深度学习的不断发展，基于深度学习的方法逐渐被引入信号处理领域以取代传统算法。本文提出一种基于卷积神经网络（CNN）的扩频信号检测方法。通过实验分析，将本文提出的CNN模型在不同信噪比情况下对DSSS信号的检测性能与传统自相关检测方法进行了比较和分析。实验验证了本文模型在不同信噪比、不同扩频码长度、不同扩频码类型以及不同调制方式下的估计性能，并与自相关检测算法进行了对比。结果发现，本文模型的检测性能高于自相关检测方法，整体性能提高了4dB。
