Li Chun, Chen Ying, Zhao Hijin
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Math Biosci Eng. 2023 Jun 2;20(7):12843-12863. doi: 10.3934/mbe.2023573.
The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.
基于时频分析的传统跳频信号检测方法的性能受到时频分辨率和频谱泄漏权衡的限制。基于机器学习的跳频信号检测技术具有较高的复杂度。因此，本文提出了一种残差网络和优化的广义S变换来检测跳频信号。首先，基于时频聚集度量，使用多种群遗传算法对广义S变换参数λ和p进行优化。其次，利用优化后的广义S变换确定信号的时频谱，然后对其进行归一化处理，使其对噪声功率不确定性具有鲁棒性。最后，设计了一种接收时频谱的残差网络结构。为了检测跳频信号，该网络自动学习信号和噪声的时频特性。仿真结果表明，与常规遗传算法相比，多种群遗传算法不仅提高了优化效率，而且收敛速度更快，优化结果更稳定。与混合卷积网络/循环神经网络算法相比，所提技术在检测方面表现更好，且计算和存储复杂度更低。
