Phase-Calibration-Based 3-D Beamspace Matrix Pencil Algorithm for Indoor Passive Positioning and Tracking

In the context of integrated sensing and communication (ISAC), indoor device-free positioning and tracking technology based on channel state information (CSI) has become a key and significant branch. CSI extracted by high-bandwidth chips is not widely used for sensing due to problems of clock asynchrony and phase locking instability. This article implements passive positioning based on the CSI collected by the bcm4366 Wi-Fi chip. First, through extensive testing and observation, we describe the unstable phase difference phenomenon of the radio frequency (RF) channel of the bcm4366 chip. Calibrating phase ambiguity (PA) is implemented by a phase detection and compensation method. Based on this, a 3-D beamspace matrix pencil (BMP) algorithm is proposed to jointly estimate the angle of arrival (AoA), time of flight (ToF), and Doppler frequency shift (DFS) of CSI dynamic targets. Finally, passive localization is achieved based on joint multiparameters. Experiments verify that the 3-D BMP algorithm is superior to the existing algorithms in terms of tradeoff estimation accuracy and computational complexity. The positioning scheme based on 3-D BMP effectively reduces the positioning error after eliminating PA. In the single-link case, the median localization errors of the activity room and meeting room scenarios reach 0.42 and 0.55 m, respectively.

I. Introduction

In recent years, due to the wide coverage and privacy of Wi-Fi signals, indoor sensing technology based on Wi-Fi channel state information (CSI) has flourished. This heralds the arrival of the era of integrated sensing and communication (ISAC) [1], [2], [3]. Many recent works have integrated sensing functions into Wi-Fi devices, using ubiquitous Wi-Fi signals to perform a series of applications in indoor scenarios, such as fine-grained vital sign monitoring [4], [5], [6], [7], [8], gesture recognition [9], [10], [11], coarse-grained motion [12], [13], fall detection [14], [15], [16], and indoor positioning and tracking [17], [18], [19], [20], [21], [22], [23]. Meanwhile, to further satisfy various applications of Wi-Fi sensing, CSI extraction platforms based on different Wi-Fi chips are constantly developed. From Intel 5300 CSI tool [24] to Atheros [25], Nexmon [26], AX-CSI [27], the bandwidth and number of subcarriers of CSI extraction platforms have gradually increased. By means of frequency hopping [25], [28] and device splicing [29], the resolution and robustness of CSI have fundamentally enhanced, which brings a shortcut to the way forward for the ISAC.