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 Aug 7;23(15):7002. doi: 10.3390/s23157002.
In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.
为了更有效地监测和干扰敌方信号，在日益复杂的电磁环境中准确、高效地识别截获信号并估计其参数尤为重要。因此，在非合作情况下，研究如何实时准确检测直接序列扩频（DSSS）信号并估计其参数具有重要的现实意义。传统的时延相关算法面临峰值能量泄漏和假峰干扰等挑战。作为一种替代方法，本文介绍了一种基于残差网络的一维卷积神经网络（CNN-ResNet）的伪噪声（PN）码周期估计方法。该方法将扩频码周期估计问题转化为扩频码长度估计的多分类问题。首先，将接收到的DSSS信号的同相/正交（I/Q）两路直接输入到CNN-ResNet模型中，该模型将自动学习不同PN码长度的DSSS信号特征，然后估计PN码长度。使用信噪比（SNR）范围为-20至10 dB的DSSS信号数据集进行仿真实验。在使用BPSK调制对模型进行训练和验证后，再用QPSK调制信号进行测试，并通过损失函数、准确率、召回率和混淆矩阵等指标分析估计性能。结果表明，本文提出的一维CNN-ResNet能够有效估计非合作DSSS信号的PN码周期，具有较强的泛化能力。
