导向到达估计和突出特征测试来自多个自主水下车辆的宽带回声
Xiaofeng Yin1, Peizhen Zhang1, Guangbo Zhou1
1College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
检测多个水下自动驾驶汽车 (AUV) 是一个挑战. 一种新的自动对焦连贯信号子空间方法准确地估计到达方向 (DOA),以改进水下多目标识别.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多个自主水下车辆 (AUV) 越来越多地用于水下操作.
- 检测和识别多个移动的水下目标带来了重大挑战.
研究的目的:
- 提出和评估一种新的方法来估计多个水下AUV的到达方向 (DOA).
- 通过分析宽带回声信号,提高水下多目标识别的准确性.
主要方法:
- 利用一个28元的传感器来记录来自湖中的多个AUV的回声信号.
- 开发并应用了一种自动对焦连贯信号子空间 (ACCSM) 方法,用于在没有事先方向信息的情况下估计DOA.
- 计算了各种态度的AUV阵列信号的空间频谱.
主要成果:
- 拟议的ACCSM方法成功地估计了来自多个AUV的宽带回声信号的DOA.
- 包括亮点在内的回声信号特征受到AUV的数量,规模和态度的影响.
- 目标的微观结构对整体回声有很大影响,影响不同角度的亮点数量.
结论:
- 该研究提供了一种更准确的方法,用于水下多目标DOA估计.
- 这些发现对改善水下多目标识别系统具有重大意义.
- 了解目标尺度和态度对回声信号的影响对于有效检测至关重要.
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