张宇, 江鹏, 郭文飞, 张丹, 韩震. 一种利用两阶段学习模型的水下阵列定位方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(12): 1889-1899. DOI: 10.13203/j.whugis20210466
引用本文: 张宇, 江鹏, 郭文飞, 张丹, 韩震. 一种利用两阶段学习模型的水下阵列定位方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(12): 1889-1899. DOI: 10.13203/j.whugis20210466
ZHANG Yu, JIANG Peng, GUO Wenfei, ZHANG Dan, HAN Zhen. An Underwater Array Localization Method Using Two-Stage Learning Model[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1889-1899. DOI: 10.13203/j.whugis20210466
Citation: ZHANG Yu, JIANG Peng, GUO Wenfei, ZHANG Dan, HAN Zhen. An Underwater Array Localization Method Using Two-Stage Learning Model[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1889-1899. DOI: 10.13203/j.whugis20210466

一种利用两阶段学习模型的水下阵列定位方法

An Underwater Array Localization Method Using Two-Stage Learning Model

  • 摘要: 水下声学(underwater acoustic, UWA)阵列信号处理是常见的水下定位方式之一。针对噪声影响定位精度的问题,提出一种利用两阶段学习模型的定位方法。首先,分别对接收信号的实部和虚部特征进行训练,建立一个基于多层卷积神经网络的学习模型进行降噪处理;然后,构建一个改进的加权延时求和的波束形成器组模型,利用梯度下降准则对各个通道的权重进行调整,得到最优相对时延和最佳角度估计,再通过几何解算得到较为精确的定位信息。仿真实验结果表明,在-25~10 dB的信噪比环境中,所提方法与传统水下声阵列处理相比,应对信噪比变化的鲁棒性更强,且定位精度更高,湖上实验进一步验证了该方法的有效性。

     

    Abstract:
      Objectives  In the localization method based on underwater acoustic arrays, noise affects the localization accuracy mainly. A localization method using a two-stage learning model is proposed to minimize noise's influence on the localization results.
      Methods  Firstly, a learning model based on a multilayer convolutional neural network is built for noise reduction by training the real and imaginary features of the received signal separately. Secondly, an improved weighted delay summation beamformer group model is constructed, and the gradient descent criterion adjusts the weights of each channel to obtain the optimal relative time delay and the best angle estimation. Finally, more accurate localization information is received by geometric solving.
      Results  Simulation experimental results show that the two-stage noise reduction model has awe-inspiring noise reduction performance compared to conventional underwater acoustic array processing for angular comparisons of 30°, 100°, and 130° in a SNR (signal-to-noise ratio) environment in the range of -25 dB to 10 dB. Lake tests show the same advantage of the proposed method in terms of RMSE (root mean square error) of positioning results.
      Conclusions  The two-stage learning model has excellent robustness in coping with SNR variations, and the received signal after processing by noise reduction has higher localization accuracy after processing based on an improved time-delay and angle estimation model, which can also be applied in practical applications such as indoor acoustic localization and radio radar arrays.

     

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