WiSpeed: A Statistical Electromagnetic Approach for Device-Free Indoor Speed Estimation

Abstract

Due to the severe multipath effect, no satisfactory device-free methods have ever been found for indoor speed estimation problem, especially in non-line-of-sight scenarios, where the direct path between the source and observer is blocked. In this paper, we present WiSpeed, a universal low-complexity indoor speed estimation system leveraging radio signals, such as commercial WiFi, LTE, 5G, etc., which can work in both device-free and devicebased situations. By exploiting the statistical theory of electromagnetic waves, we establish a link between the autocorrelation function of the physical layer channel state information and the speed of a moving object, which lays the foundation of WiSpeed. WiSpeed differs from the other schemes requiring strong line-of-sight conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, WiSpeed saves the users' efforts from large-scale training and fine-tuning of system parameters. In addition, WiSpeed could extract the stride length as well as detect abnormal activities such as falling down, a major threat to seniors that leads to a large number of fatalities every year. Extensive experiments show that WiSpeed achieves a mean absolute percentage error of 4.85% for device-free human walking speed estimation and 4.62% for device-based speed estimation, and a detection rate of 95% without false alarms for fall detection.

Radio Frequency Based Direction Sensing Using Massive MIMO

“…5 show that it is tolerable. Jointly using (13) and (18) and considering all the (i, m) pairs, we can get that…”

“…Different from most existing works which rely on dedicated devices, this paper is inspired by the principle of time reversal (TR), based on which many powerful indoor target localization and tracking methods have been developed [13]- [18]. In particular, the time-reversal resonating strength (TRRS) [14] is proved to be a stationary and location-independent focusing-ball shaped distribution around the receiver [17].…”

“…In particular, the time-reversal resonating strength (TRRS) [14] is proved to be a stationary and location-independent focusing-ball shaped distribution around the receiver [17]. By leveraging such an observation, a target tracking method with centimeter-level accuracy has been proposed and verified by extensive experiments [18].…”

“…Intuitively, the afore-mentioned indoor localization systems [17], [18] take advantage of the massive multipath components existing in the indoors and establish a link between the statistical property of the received signal and the moving speed of the target. However, when it comes to outdoor scenario with cellular communication being widely deployed, there are two new challenges.…”

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

“…In the past few years, WiFi signals have been exploited for localization [10], [11], [12], [13], [14], activity tracking [5], [6], [15], [16], [17], [18], [19], gesture recognition [20], [21], [22], [23] and recently more fine-grained respiration monitoring [24], [25], [23], [26] and material sensing [27]. Even though non-periodical fine-grained activities such as keystrokes, finger gestures and mouth speaking have been reported to be sensed using WiFi Channel State Information (CSI), they all rely on one assumption that the position of the moving target relative to the WiFi transceivers is fixed such that the same movement would lead to similar signal change patterns in different rounds.…”