基于具有时间结构的神经网络的到达方向估计方法,用于水下声学矢量传感器阵列
1School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了先进的深度学习方法,LSTM-ATT和变压器,以改进水下声向量传感器到达方向估计. 这些技术显著提高了准确性,特别是在信号噪声比较低的环境中.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器学习用于传感器阵列.
背景情况:
- 声向量传感器 (AVS) 对于水下检测至关重要.
- 传统的使用共变矩阵的到达方向 (DOA) 估计方法遭受信号定时损失和噪声免疫力低下的困扰.
- 现有的方法在低信号噪声比 (SNR) 条件下难以准确.
研究的目的:
- 为水下AVS阵列提出基于深度学习的新DOA估计方法.
- 为了解决传统的基于共差的DOA估计技术的局限性.
- 为了提高在具有挑战性的水下声环境中DOA估计的准确性和稳定性.
主要方法:
- 开发一种DOA估计方法,利用一个带有注意力机制的长期短期记忆网络 (LSTM-ATT).
- 基于变压器架构的DOA估计方法的开发.
- 与传统的多重信号分类 (MUSIC) 方法进行比较分析.
主要成果:
- 与MUSIC相比,LSTM-ATT和变压器方法都显示出更高的性能,特别是在低SNR场景中.
- 基于变压器的方法实现了与LSTM-ATT方法相比的DOA估计准确度.
- 变压器方法的计算效率明显优于LSTM-ATT方法.
结论:
- 深度学习方法,特别是LSTM-ATT和Transformer,在水下AVS DOA估计中提供了显著的改进.
- 基于变压器的方法为在低SNR条件下快速有效的DOA估计提供了有希望的解决方案.
- 这些先进的方法提高了水下探测系统的可靠性.
更多相关视频
11:14A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
Published on: October 4, 2015
11.0K
07:14Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
Published on: May 1, 2018
7.8K
相关概念视频
01:29Application of Linearization and Approximation
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
01:30Vector Functions and Motion: Problem Solving
Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...