将方位估计问题看成多标签分类问题，将稠密连接网络应用于单矢量水听器目标方位估计，使用经典方法中广泛关注的二阶统计量作为神经网络的输入，使用不断生成训练集的方法训练神经网络。仿真及湖试结果表明，使用稠密连接网络相较经典方法具有更窄的主瓣和更高的方位分辨力；在2个目标信噪比相差6 dB及以上的情况下，稠密连接网络具备经典方法没有的同时检测2个目标的能力，且仍具有优秀的方位分辨力；当2个目标信噪比相差大于18 dB后稠密连接网络逐渐丧失了对弱目标的检测能力。
Consider the problem of bearing estimation as a multi label classification problem, apply dense connected networks to single vector hydrophone target bearing estimation, use second-order statistics widely concerned in classical methods as input to the neural network, and train the neural network using the method of continuously generating training sets. The simulation and lake trial results show that using DenseNet has narrower main lobes and higher azimuth resolution compared to classical methods; When the signal-to-noise ratio of two targets differs by 6 dB or more, the DenseNet has the ability to simultaneously detect two targets that classical methods do not have, and still has excellent azimuth resolution; When the signal-to-noise ratio difference between two targets exceeds 18 dB, the DenseNet gradually losses its ability to detect weak targets.
2024,46(12): 132-139 收稿日期：2023-08-17
DOI：10.3404/j.issn.1672-7649.2024.12.023
分类号：U666.7
作者简介：柯凯磊(1999-),男,硕士,助理工程师,研究方向为水声信号处理
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