Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information

Abstract

P2V (pedestrian-to-vehicle) communication, in which a pedestrian’s mobile device notifies its position to nearby vehicles in order to prevent pedestrian accidents, has attracted much research interest recently, but its performance largely depends on the precision of pedestrians’ positioning. Pedestrian positioning is generally performed by using GPS (Global Positioning System), and its precision may greatly degrade in urban canyons. To improve positioning precision of pedestrians, it was proposed that vehicles around pedestrians be used as anchors beside satellites. In this method, a pedestrian device overhears V2V (vehicle-to-vehicle) communication signals which carry the vehicle position and calculates a pedestrian’s position using pedestrian–vehicle distance/angle information estimated from CSI (channel state information) of the V2V signals. However, angle estimation typically depends on the number of antennas of the pedestrian device. In this paper, we propose a new method to estimate signal radiation angle by tracking temporal change of CSI caused by vehicle movement during signal transmission and investigate its application to precise pedestrian positioning. Three-dimensional ray tracing simulations confirm that compared to the base method using eight antennas, the proposed method with a single antenna reduces average angle error from 13 degrees to 3.9 degrees, and reduces average positioning error from 2.49 m to 0.78 m.

Precise Angle Estimation by Jointly Using Spatial/Temporal Change of Channel State Information and Its Application in Pedestrian Positioning

“…Similar to J-CSI, the method [26] uses the CSI in the frequency axis together with the CSI of multiple antennas to construct a two-dimensional virtual antenna array, but only the CSI change among multiple antennas is used for the AoA estimation. In comparison, J-CSI is different in that it uses CSI in both the spatial/temporal axes for the AoA estimation by using the relationship between AoA/AoD of the LoS wave (described in (12)). These features do not exist in other literature.…”

“…Different from the previous methods that exploit multiple antennas, AoD is estimated using CSI acquired at different timings by utilizing the fact that vehicles move while transmitting the V2V communication signals [12]. When acquiring multiple CSI from multiple packets, accurate time synchronization between the transmitter and the receiver is required.…”

“…CSI from multiple antennas (spatial) [11] and from multiple receiving timings (temporal) [12] can be acquired simultaneously, but were not used together in previous work because the two methods use different theories for the angle estimation. In this paper, we investigate how to combine the two to compute more accurate 2D angle information, especially when the number of antennas is small.…”

“…Figure 3 shows a comparison between the base methods [11][12] and the proposed method. Hereafter, for simplicity, the base methods and the proposed method are referred to as S(Spatial)-CSI [11], T(Temporal)-CSI [12], and J(Joint)-CSI, respectively.…”

“…On this basis, other methods [11][12] were also proposed, which use pedestrian-vehicle angle information in addition to distance information. In [11], angle of arrival (AoA) is estimated from spatial differences of CSI in multiple receiving antennas, and in [12], angle of departure (AoD) is estimated from temporal differences of CSI at multiple receiving timings in a single antenna. But the precision of angle estimated by these methods is still limited because (i) only a small number of antennas are available at a pedestrian device for the AoA estimation and (ii) only temporal change of CSI within a short interval is available for the AoD estimation.…”