井冈山大学学报（自然科学版）2026，Vol.47Issue(5)：81-88,8.DOI:10.3969/j.issn.1674-8085.2026.05.009
基于神经网络定位算法的系统设计与仿真
System design and simulation of neural network-based localization algorithm
摘要
Abstract
To address the challenges of target positioning in small underwater areas,this paper proposes a high-precision underwater positioning system featuring deep hardware-software synergy.On the hardware side,corresponding hardware designs have been finalized after simulation verification.The underwater acoustic beacon can theoretically maintain an average operating power consumption of 76 microwatts with the costs reduced to the 10-yuan level.Meanwhile,a wideband variable gain amplifier(VGA)is deployed at the receiving terminal to perform dynamic signal compression to acquire high-fidelity waveform data.Algorithmically,a lightweight Slim U-Net-based method is proposed to extract the probability distribution of the direct wave Time of Arrival(ToA).Combined with sub-sampling,this achieves nanosecond-level delay estimation.To mitigate multipath-induced non-line-of-sight(NLOS)outliers,a hybrid nonlinear optimization engine integrating a Soft-L1 robust loss boundary and trust-region constraints is cascaded,which effectively eliminate positioning coordinate divergence.Monte Carlo 3D acoustic field simulations demonstrate that,while sustaining milliwatt-level power consumption,the system tightly constrains the near-field dynamic tracking error(95%confidence interval)at 0.040 m,and stabilizes the global RMSE at 0.111 m.This significantly enhances the spatial immunity in outliers and positioning accuracy for complex underwater environments.关键词
信号与信息处理/水下定位系统/到达时间差/U-Net神经网络/多途抑制Key words
signal and information processing/underwater positioning system/time difference of arrival/U-Net neural network/multipath suppression分类
通用工业技术引用本文复制引用
陈磊,郑红..基于神经网络定位算法的系统设计与仿真[J].井冈山大学学报（自然科学版）,2026,47(5):81-88,8.基金项目
国家重点研发计划海洋环境安全保障项目(2017YFC1403500) （2017YFC1403500）
