双模型联合优化的水声通信信号检测与识别算法
Detection and recognition algorithm for underwater acoustic communication signals based on dual model joint optimization
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摘要: 为了应对非合作水声通信信号识别因水声信道时变特性带来的干扰, 同时适应低功耗部署的需求, 深度学习识别需要轻量化设计, 提高识别精度, 增强模型泛化能力。首先提出了一种基于DenseNet结构改进的轻量高效识别模型, 通过采用维度转换和模型压缩方法, 优化模型结构及参数, 在保证识别精度的同时降低模型推理复杂度; 其次采用一种多模态表达的融合策略, 通过有效结合不同网络提取的特征, 充分利用了信息的互补性, 从而显著提高识别准确性。在仿真数据集上, 融合网络在−6 dB信噪比及以上的识别率超过94.65%, 在0 dB时达到98.03%。在实测数据集上, 基础网络经迁移学习后的精度达到了98.05%, 湖上试验结果验证了本文所提方法的有效性。Abstract: In order to address the interference caused by the time-varying characteristics of underwater acoustic channels in non-cooperative underwater acoustic communication signal recognition and to meet the needs of low-power deployment, deep learning recognition requires lightweight design to improve recognition accuracy and enhance model generalization ability. A lightweight and efficient recognition model is proposed firstly based on an improved DenseNet structure. By adopting dimension transformation and model compression methods, the model structure and parameters are optimized, reducing the complexity of model inference while ensuring recognition accuracy. Secondly, a multi-modal expression fusion strategy is employed, effectively combining features extracted by different networks, fully utilizing the complementarity of information, thereby significantly improving recognition accuracy. On the simulated dataset, the fusion network achieves a recognition rate of over 94.65% at a signal-to-noise ratio of −6 dB and 98.03% at 0 dB. On the real measured dataset, the accuracy of the base network after transfer learning reaches 98.05%. Lake test results validate the effectiveness of the proposed method.