用单麦克风实现高精度行人速度估计，突破多普勒效应限制。
ASE: Practical Acoustic Speed Estimation Beyond Doppler via Sound Diffusion Field
- 基于声扩散场建模，不依赖多普勒频移，改用空间分布推算速度。
- 采用高速正交时延复用技术，实现高采样率通道估计，支持高速运动捕捉。
- 在4m×4m空间中实测误差仅0.13m/s，检测率达97.4%，适合实际部署。
被动式人体速度估计在声学传感中至关重要。现有系统存在两大局限：一是信道测量速率不足，难以估算高速运动；二是依赖麦克风阵列和多普勒频移（DFS），仅能感知有限距离内的径向速度。为此，我们提出ASE系统，利用单个商用麦克风实现精准可靠的声学速度估计。通过新型正交时延复用（OTDM）方案，实现此前无法达到的高速信道估计，支持高速运动追踪。创新性地从声扩散场角度建模声波传播，通过声场空间分布推断速度，完全突破传统DFS方法。进一步设计运动检测与信号增强技术，提升系统鲁棒性。通过大量真实场景实验验证，结果表明：在4m×4m空间内自由行走时，平均误差仅为0.13 m/s，较DFS降低2.5倍，检测率达97.4%。我们相信，ASE推动声学速度估计超越传统多普勒范式，为声学传感带来新方向。代码已开源：https://github.com/aiot-lab/ASE。
原文摘要 · Abstract (English)
Passive human speed estimation plays a critical role in acoustic sensing. Despite extensive study, existing systems, however, suffer from various limitations: First, the channel measurement rate proves inadequate to estimate high moving speeds. Second, previous acoustic speed estimation exploits Doppler Frequency Shifts (DFS) created by moving targets and relies on microphone arrays, making them only capable of sensing the radial speed within a constrained distance. To overcome these issues, we present ASE, an accurate and robust Acoustic Speed Estimation system on a single commodity microphone. We propose a novel Orthogonal Time-Delayed Multiplexing (OTDM) scheme for acoustic channel estimation at a high rate that was previously infeasible, making it possible to estimate high speeds. We then model the sound propagation from a unique perspective of the acoustic diffusion field, and infer the speed from the acoustic spatial distribution, a completely different way of thinking about speed estimation beyond prior DFS-based approaches. We further develop novel techniques for motion detection and signal enhancement to deliver a robust and practical system. We implement and evaluate ASE through extensive real-world experiments. Our results show that ASE reliably tracks walking speed, independently of target location and direction, with a mean error of 0.13 m/s, a reduction of 2.5x from DFS, and a detection rate of 97.4% for large coverage, e.g., free walking in a 4m x 4m room. We believe ASE pushes acoustic speed estimation beyond the conventional DFS-based paradigm and inspires exciting research in acoustic sensing. Code is available at https://github.com/aiot-lab/ASE.
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