雷达Former:通过墙壁雷达和变压器进行端到端的人类感知
IEEE transactions on neural networks and learning systems
|September 22, 2023
概括
雷达Former是一种新的方法,使用雷达回声上的自我注意力 (SA),增强人类感知任务,如姿势估计. 它在性能和效率上优于现有的雷达方法,即使在具有挑战性的环境中也是如此.
科学领域:
- 计算机视觉 计算机视觉
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 雷达传感器在具有挑战性的环境中为人类感知提供了优势,比光学摄像机优越,例如低可见度和隐私敏感的设置.
- 目前基于雷达的人类感知通常依赖于将回声转化为图像,以便使用卷积神经网络提取特征.
- 这些成像步骤可能是计算密集的,可能无法充分利用雷达信号的固有特征.
研究的目的:
- 介绍RadarFormer,这是第一个将自我注意力 (SA) 机制直接应用于人类感知任务的雷达回声的方法.
- 为了证明端到端处理雷达信号的有效性,绕过传统的成像算法.
- 在基于雷达的人类感知中建立一个新的最先进的状态,以提高性能和计算效率.
主要方法:
- 开发了RadarFormer,一种类似于变压器的模型,利用针对雷达信号特征的快速/缓慢时间自我注意 (SA) 机制.
- 提供了理论证明,SA对雷达回声的处理至少与对雷达图像的卷积处理一样富有表现力.
- 从雷达回声直接启用端到端的信号处理,消除了中间成像步骤的需要.
主要成果:
- 与现有的基于雷达的最先进方法相比,RadarFormer在人类感知任务中取得了更高的性能.
- 提出的方法证明了计算成本的显著改善.
- 即使在具有挑战性的条件下,也获得了准确的人类感知结果,包括黑暗和封闭的环境.
结论:
- 雷达形式代表了基于雷达的人类感知的重大进步,通过直接利用回声信号来提高自我注意力.
- 端到端的方法为传统的基于图像的雷达处理提供了更高效和有效的替代方案.
- 该方法对在多样化和困难的环境条件下可靠的人类传感有很大的希望.
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