摘要
针对被动声纳系统中不规则波动线谱检测准确率低的问题,提出一种基于U-net网络改进的线谱检测方法。以U-net作为网络主体框架,引入残差结构,增加网络的深度,加强模型特征学习能力。同时在编码器部分引入特征通道注意力机制,使模型学习到通道之间的不同特征的重要程度,从而提升模型的特征表达能力。最后,在解码器部分采用DUpsampling上采样方法,利用分割标签空间中的冗余能力,更准确的恢复线谱像素级预测。将改进模型与HMM模型和CEM模型在线谱检测效果上进行比较。实验结果表明,在信噪比为-24~-20 dB下,改进模型线谱检测准确率为0.314~0.526,优于HMM模型和CEM模型。
A line spectrum detection method improved by U-net network is proposed, which is aiming at the problem of low detection accuracy of the irregular fluctuating line spectrum in passive sonar system. The system framework is based on U-net network, and the residual structure is introduced to increase the depth of the network and strengthen the learning ability of the model feature. Meanwhile, the feature channel attention mechanism is brought in the encoder part which can help the model learn the importance of different features between channels, thus improving the feature expression ability of the model. Finally, the DUpsampling up-sampling method is used in the decoder part, and the redundancy capability in the segmented label space is utilized which can accurately restore the pixel-level prediction. The improved model is compared with the HMM model and CEM model on the line spectrum detection effect. Under the SNR of-24~-20 dB, the accuracy of line spectrum detection of the improved model is 0.314~0.526, which is better than that of HMM model and CEM model.
作者
裴明
陈阳
邓林红
Pei Ming;Chen Yang;Deng Linhong(School of Microelectronics and Control Engineering,Changzhou University,Changzhou 213164,China;School of Biomedical Engineering and Health Sciences,Changzhou University,Changzhou 213164,China)
出处
《电子测量技术》
北大核心
2021年第13期85-90,共6页
Electronic Measurement Technology
基金
声纳技术国防科技重点实验室基金(6142109180206)项目资助。
