Indoor Drone Propeller Speed Estimation Using Wi-Fi Channel State Information

International Conference · 2025

Indoor Drone Propeller Speed Estimation Using Wi-Fi Channel State Information

Keywords

WiFi CSIDrone

Yuta Takao , Viktor Erdelyi , Kazuya Ohara , Yasue Kishino , Anh-Van Ho , Akira Uchiyama

The 21th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2025)

Research Note

With the evolution of drone technology, its range of applications has expanded to include logistics, surveillance, inspection, agriculture, and other indoor applications. However, improving drone control accuracy has become a major challenge, and accurate knowledge of propeller rotation speed is essential to solving this problem.
In this study, we propose a method for estimating propeller rotation speed using Wi-Fi Channel State Information (CSI). This method has the advantage that it does not require any additional hardware because it utilizes the Wi-Fi equipment installed in the drone.
The proposed method applies a Fast Fourier Transform (FFT) to CSI amplitude data to perform frequency analysis and peak detection. To cope with the non-uniform observation intervals of CSI, a resampling process is introduced to improve the estimation accuracy.
In the future, we plan to evaluate the system on actual drones to achieve fast RPM estimation on the millisecond order.

Related Lab Activities

Other Research Topics

Environment-Aware Distributed Scheduling for Emergency LoRa Networks

Yuto Inaba, Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), SPT-IoT 2026, pp. 1366–1371

DOI 10.1109/PerComWorkshops68308.2026.11585469

Disaster CommunicationLoRa +4

A Lightweight Vision-Language Model for Disaster Image Summarization

Hibiki Yoshizaki, Akira Uchiyama, Akihito Hiromori, Mineo Takai, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerconAI 2026, pp. 1203–1208

DOI 10.1109/PerComWorkshops68308.2026.11585419

Semantic CommunicationDisaster Response +4

Physics-Integrated Deep Learning for Urban Landslide Prediction

Ren Ozeki, Hamada Rizk, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), URBSENSE 2026, pp. 1094–1099

DOI 10.1109/PerComWorkshops68308.2026.11585337

Landslide PredictionPhysics-Integrated Learning +3

A Simulation Framework for Precision Formation Flying of Massive Satellite Swarms

Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi, Sumio Morioka

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerVehicle , pp. 230–235

DOI 10.1109/PerComWorkshops68308.2026.11585321

Satellite Formation FlyingDistributed Simulation +4

Ray-Tracing-Driven Pattern-Based Vehicle Recognition in ISAC Radar

Heetae Jin, Akira Uchiyama

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerRad 2026, pp. 328–333

DOI 10.1109/PerComWorkshops68308.2026.11585327

ISACBeyond 5G +4

A Questionnaire-Only Counterfactual Machine Learning Approach to Assess the Spatial Impact of Green Mobility Vehicles in Urban Parks

Rami Naeem, Srikant Manas, Tatsuya Amano, Hirozumi Yamaguchi

ICDCN 2026 Workshop: IWNDSC2026

DOI 10.1145/3737611.3776620