ASPIRE Workshop 2024
1

A Research of Kalman Filter Enabled Beam Tracking for Multiple
VehiclesDou HU† , Student Member, Jin NAKAZATO† , Ehsan JAVANMARDI† , Muhammad ASAD† , Members,Kazuki MARUTA†† , Senior Member, and Manabu TSUKADA† , Member

SUMMARY
In the era of Beyond 5G, the significance of interdisciplinary research has become increasingly important. Within this context,
the Kalman filter, a technology integral to self-positioning estimation in autonomous driving, is already being adopted in various societal applications.
This study proposes a method wherein beam tracking, in conjunction with
the Kalman filter, is an alternative to GPS in specific scenarios. This research is particularly relevant in environments such as intersections flanked
by high-rise buildings, where GPS signals are prone to interference.
key words: Beyond 5G, Kalman filter, beam tracking, V2X, position detection

1.

Introduction
Fig. 1

The advancement of Beyond 5G (B5G) technology has significantly elevated the importance of precise positioning
and speed detection for vehicles related to vehicular-toeverything (V2X) [1]. As a result, numerous studies areconcentrating on delivering more accurate vehicle location
information. While the Global Positioning System (GPS)offers highly real-time location data, it faces challenges inenvironments with less-than-ideal conditions, such as intersections surrounded by high-rise buildings, large structures,and various other obstacles. In these scenarios, beam tracking technology is the most viable alternative [2].
The proposed framework [3] is designed for deployment in a scenario involving multiple roadside units (RSUs)
and vehicles. Initially, we operate assuming that each RSU
has a beam tracking function. Additionally, we focus on aspecific condition at an intersection. As depicted in Fig.1,
the intersection is our research scope area, and the operationof the framework there unfolds in the following steps:
Step 1: The vehicle sends a reference signal to the RSU.
Step 2: The RSU receives the reference signal and sends thereceived data to the Multi-access Edge Computing (MEC)
node.
Step 3: Data analysis is performed at the MEC. The analysis
determines the propagation channel, the vehicle’s position,and speed using the proposed method.
Step 4: The results (propagation channel, the vehicle’s position, and speed) obtained are fed back to the RSU.

2.

Proposed architecture.

System modelThis section describes the system model. In the results derived from Steps 1 to 4, as detailed in the preceding section,
beam tracking hinges on the precise utilization of the vehicle’s channel data, along with its position and speed information. As the vehicle moves, its speed is tracked by theRoadside Unit (RSU) using beam tracking technology. This
process involves the vehicle transmitting a reference signal,
which the RSU then analyzes to ascertain the vehicle’s position and speed. The analysis of this data is further refinedwith an extended Kalman filter (EKF). The methodology
underlying this process is encapsulated in Eqs. (1) and (2).
The first step in applying the Kalman filter involves estimating the channel parameters. This is accomplished bydetermining the Direction of Arrival (DoA) and the Timeof Arrival (ToA), which collectively describe the channel’scharacteristics, as shown in Eq. (1). Subsequently, the second step employs the Kalman filter to estimate the vehicle’sposition and velocity. The equations governing this estimation process are represented by Eq. (2). In this equation,
Φ𝑡 ,𝑡 −1 means the transfer equation of the system. And the𝑠 [𝑡] is the parameter vector of the time t, which including theazumith elevation DoA and the ToA of the vehicle. 𝐻 (𝑠 [𝑡])is the measurement function for the channel parameter estimation. 𝑍 [𝑡] is the measurement parameter. 𝑥 [𝑛] is thevehicle’s parameter and Φ𝑡 ,𝑡 −1 is the measurement parameter for the vehicle part, and finally the matrix A means theacceleration equation for the equation.

† The author is with Graduate School of Information Scienceand Technology, The University of Tokyo, 1-1-1, Yayoi, Bunkyoku, Tokyo, 113-8657 Japan†† The author is with Tokyo University of Science, 6-3-1 Niijuku,Katsushika-ku, Tokyo 125-8585, Japan.

Copyright © 2024 The Institute of Electronics, Information and Communication Engineers
22

ASPIRE Workshop 2024
2





3.

𝑠 [𝑡] = Φ𝑡 ,𝑡 −1 𝑠 [𝑡 − 1] + 𝑤 [𝑡]𝑍 [𝑡] = 𝐻 (𝑠 [𝑡]) + 𝑚 [𝑡],(1)𝑥 [𝑛] = 𝐹𝑥 [𝑛 − 1] + 𝐴𝑎[𝑛 − 1] + 𝑢[𝑛]b + [𝑛] = h(x[𝑛]) + v[𝑛].
𝚯(2)Scenario

This section explains the evaluated scenario. We deploy theenvironment in MATLAB and we set the carrier frequency
as 28GHz, bandwidth as 100MHz and subcarrier spacing as60kHz. We have given 3 conditions of the beam tracking
as Fig. 2. Fig. 2 (a) depicts the beam sweep mode where
the RSU’s beams are optimally aligned with both vehicles
simultaneously, providing ideal tracking for both vehicle 1and vehicle 2. Fig. 2 (b) shows the beam sweep mode
when the RSU’s beam is optimally directed towards vehicle
1, allowing for precise tracking of its position and speed.
Besides, Fig. 2 (c) represents the beam sweep mode when
the RSU’s beam is best aligned with vehicle 2, focusing on
accurately tracking the second vehicle’s movements. These
descriptions assume the RSU is using different directional
beams to track the vehicles’ positions and velocities, and eachfigure demonstrates a different scenario of beam tracking.
4.

(a) Two vehicles are the best direction.

Conclusion

In this study, we have proposed an architecture and an operational scenario in V2X. Furthermore, it has been demonstrated that the proposed method can deliver accurate positioning results. These results include tracking the propagation channel during movement and determining the vehicle’s
position and velocity. In future works, we aim to expand thismethod to accommodate multiple antenna conditions andevaluate its performance within complex traffic models.

(b) Vehicle 1 is the best direction.

Acknowledgments
This research is supported by the Strategic Information andCommunications R&D Promotion Programme (SCOPE) by
Ministry of Internal Affairs and Communications, Japan (receipt number JP235003002).
References[1] Y. Asabe et al., ”AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving,”
2023 IEEE 97th Vehicular Technology Conference (VTC2023Spring), Florence, Italy, 2023, pp. 1-7, doi: 10.1109/VTC2023Spring57618.2023.10199425.
[2] M. Koivisto et al., “Channel Parameter Estimation and TX Positioning With Multi-Beam Fusion in 5G mmWave Networks,” IEEE
Transactions on Wireless Communications, vol. 21, no. 5, pp. 31923207, 2022, doi: 10.1109/TWC.2021.3119227.
[3] H. Dou et al., ”An Extended Kalman Filter Enabled Beam Tracking
Framework in Intersection Management,” European Conference on
Networks and Communications (EuCNC) & 6G Summit, June 2023.

(c) Vehicle 2 is the best direction.
Fig. 223

Sweep mode in intersection management.