Wi-Painter

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3633809
Dawei Yan, Panlong Yang, Fei Shang, Weiwei Jiang, Xiang-Yang Li
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引用次数: 0

摘要

WiFi 已逐渐发展成为室内环境传感的主要候选技术之一。在本文中,我们希望利用 COTS WiFi 设备来识别周围环境中静止物体的材料细节,包括位置、材料类型和形状,这可能会为许多应用带来新的机遇。具体来说,我们提出的 Wi-Painter 是一个模型驱动的系统,无需修改即可使用 COTS WiFi 设备准确检测光滑表面的材料类型及其边缘。与以往的材料识别技术不同,Wi-Painter 将目标细分为单个二维像素,同时根据识别每个像素的材料类型形成二维图像。Wi-Painter 的主要思想是利用物体表面的复介电常数,通过不同偏振方向信号的不同反射率来估计物体表面的复介电常数。特别是,我们构建了多入射角模型来表征材料,仅使用在几个不同入射角测量到的垂直和水平极化信号的功率比,这避免了使用不准确的 WiFi 信号相位。我们在现实世界中实施并评估了 Wi-Painter,结果表明,在不同环境下,对不同材料类型(包括不同尺寸和厚度的金属、木材、橡胶和塑料)的平均分类准确率为 93.4%。此外,Wi-Painter 还能准确检测出不同材料拼接的 "LOVE "一词的材料类型和边缘,其平均尺寸为 60 厘米 × 80 厘米,材料边缘的方向也各不相同。
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Wi-Painter
WiFi has gradually developed into one of the main candidate technologies for indoor environment sensing. In this paper, we are interested in using COTS WiFi devices to identify material details, including location, material type, and shape, of stationary objects in the surrounding environment, which may open up new opportunities for many applications. Specifically, we present Wi-Painter, a model-driven system that can accurately detects smooth-surfaced material types and their edges using COTS WiFi devices without modification. Different from previous arts for material identification, Wi-Painter subdivides the target into individual 2D pixels, and simultaneously forms a 2D image based on identifying the material type of each pixel. The key idea of Wi-Painter is to exploit the complex permittivity of the object surface which can be estimated by the different reflectivity of signals with different polarization directions. In particular, we construct the multi-incident angle model to characterize the material, using only the power ratios of the vertically and horizontally polarized signals measured at several different incident angles, which avoids the use of inaccurate WiFi signal phases. We implement and evaluate Wi-Painter in the real world, showing an average classification accuracy of 93.4% for different material types including metal, wood, rubber and plastic of different sizes and thicknesses, and across different environments. In addition, Wi-Painter can accurately detect the material type and edge of the word "LOVE" spliced with different materials, with an average size of 60cm × 80cm, and material edges with different orientations.
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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