用深度学习识别空中声学反射体组合，提升定位信息容量。
Towards In-Air Ultrasonic QR Codes: Deep Learning for Classification of Passive Reflector Constellations
- 设计多标签CNN，从单次三维声呐数据中同时识别多个近距反射体。
- 在小规模数据集上验证了复杂声学模式解码的可行性。
- 适合做高信息密度声学地标系统的研究者参考。
在视觉传感器失效的环境中，空中声呐为自主系统提供可靠替代方案。尽管以往研究已成功分类单一声学地标，本文进一步提升信息容量，提出以反射体星座作为编码标签。主要贡献是设计一种多标签卷积神经网络（CNN），可从一次空中3D声呐测量中同时识别多个紧密排列的反射体。初步实验在小规模数据集上验证了该方法的可行性，证明了对复杂声学模式的解码能力。其次，研究采用自适应波束成形与零点抑制技术，分离单个反射体以实现单标签分类。最后，讨论实验结果与局限性，提出未来发展方向：构建信息熵显著更高的声学地标系统，并实现其精准、鲁棒的检测与分类。
原文摘要 · Abstract (English)
In environments where visual sensors falter, in-air sonar provides a reliable alternative for autonomous systems. While previous research has successfully classified individual acoustic landmarks, this paper takes a step towards increasing information capacity by introducing reflector constellations as encoded tags. Our primary contribution is a multi-label Convolutional Neural Network (CNN) designed to simultaneously identify multiple, closely spaced reflectors from a single in-air 3D sonar measurement. Our initial findings on a small dataset confirm the feasibility of this approach, validating the ability to decode these complex acoustic patterns. Secondly, we investigated using adaptive beamforming with null-steering to isolate individual reflectors for single-label classification. Finally, we discuss the experimental results and limitations, offering key insights and future directions for developing acoustic landmark systems with significantly increased information entropy and their accurate and robust detection and classification.
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