| 王珍珠,赵猛,任群言,肖旭,马力.卷积神经网络主动目标方位估计[J].,2023,42(3):467-473 |
| 卷积神经网络主动目标方位估计 |
| Active target azimuth estimation based on convolutional neural network |
| 投稿时间：2022-02-17 修订日期：2023-04-26 |
| 中文摘要: |
| 复杂海洋环境中信道的传输特性、时空变化、频散效应等一定程度上制约了主动声呐目标方位估计的性能。该文引入卷积神经网络(CNN)，提出了适用于主动声呐中目标方位的高精度估计方法。仿真声场环境为浅海负梯度，主动发射信号为具有多普勒不变性质的双曲调频信号，水平线列阵作为接收装置，目标按仿真路线运动。该文利用Kraken进行声场数据仿真，并对接收的信号在频域做均匀加权常规波束形成，进而进行卷积神经网络的模型训练和测试。数值仿真研究表明，该文所用方法可以有效估计目标波达方向，对信噪比具有一定的鲁棒性。 |
| 英文摘要: |
| The performance of active sonar target azimuth estimation is restricted to some extent by channel transmission characteristics, temporal and spatial variation and dispersion effect in complex ocean environment. In this paper, a high precision estimation method for target orientation in active sonar is proposed used convolutional neural network (CNN). The simulated acoustic field environment is shallow sea negative gradient, the active transmitting signal is hyperbolic frequency modulation signal with Doppler invariant property, the horizontal line array is the receiving device, and the target moves according to the simulated route. In this paper, Kraken is used for sound field data simulation, and the received signal is uniformly weighted conventional beamforming in the frequency domain, and then the model training and testing of CNN network is carried out. Numerical simulation results show that the proposed method can effectively estimate the target arrival direction and is robust to the signal-to-noise ratio. |
| DOI：10.11684/j.issn.1000-310X.2023.03.003 |
| 中文关键词: 主动声呐,卷积神经网络,常规波束形成 |
| 英文关键词: Active sonar, Convolutional neural network, Conventional beamforming |
| 基金项目: |
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