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论文类型：期刊论文

发表时间：2019-11-15

发表刊物：IEEE SENSORS JOURNAL

收录刊物：EI、SCIE

卷号：19

期号：22

页面范围：10662-10673

ISSN号：1530-437X

关键字：Device-free; tracking; localization; Doppler velocity; AOA; compressive sensing

摘要：Device-free localization and tracking (DFT) is a novel technique which can estimate the location of a target without equipping it with any devices. Existing DFT systems mainly rely on machine learning techniques with a labor-intensive training. Recently some training-free DFT systems are designed by deriving the angle-of-arrival (AOA) using the dedicated hardware. However, limited by the hardware imperfection, realizing a training-free DFT system using the commodity Wi-Fi is still a challenging task to solve. To address this issue, we develop a novel DFT system which can track the target motion via both target velocity and AOA estimations simultaneously. First, the motion-induced phase shifts are refined from extremely noisy channel state information (CSI) measurements to detect the Doppler shift based on the signal superposition analysis. Then, according to the phased-array signal processing, we realize joint Doppler velocity and AOA estimation of the target path under the compressive sensing framework. It formulates joint Doppler velocity and AOA estimation as a two-dimensional sparse reconstruction problem, which can achieve a high accuracy to further estimate the target velocity and track the target motion at a decimeter level. We implement the proposed DFT system on commercial Wi-Fi devices and validate its performance with extensive evaluations in three indoor scenarios.