DOI: 10.5061/dryad.18931zd4g
title: AERPAW Find-a-Rover (AFAR) Challenge in December 2023

description: Introduction In December 2023, the AERPAW Find-a-Rover (AFAR) Challenge
 marked a significant advancement in the field of unmanned aerial and
 ground vehicle collaboration. The challenge, hosted by AERPAW (Aerial
 Experimentation and Research Platform for Advanced Wireless), focused on
 the integration of cutting-edge technologies in unmanned aerial vehicles
 (UAVs) and unmanned ground vehicles (UGVs). Objective of the Challenge The
 primary objective of the AFAR Challenge was to demonstrate the capability
 of UAVs in accurately and swiftly localizing a UGV. Competitors were
 tasked with utilizing a UAV equipped with a software-defined radio (SDR)
 to detect and localize the UGV. The SDR on the UAV was designed to
 continuously receive a specific channel-sounding waveform, as detailed in
 the GE2 example experiment from the AERPAW user manual. Technical
 Specifications and Constraints Waveform Characteristics: The challenge
 mandated the use of a narrowband waveform with a bandwidth of 125 KHz.
 Competitors were restricted from altering the waveform parameters at the
 UGV, ensuring a standardized test environment. Antenna Configuration: The
 system setup included one transmit antenna and one receiver antenna, with
 the antenna patterns for both being provided to the participants.
 Environmental Data: Competitors were also given a geographical map of the
 environment to aid in the strategic deployment of the UAV. Challenge
 Execution Participants in the challenge had the flexibility to either use
 fixed waypoints for the UAV or develop their own algorithms for trajectory
 updates. These algorithms could instruct the UAV on the next waypoint to
 fly to, based on the observed signal strength received from the UGV. While
 the experiments have been executed and the data has been collected by the
 AERPAW Operations team in the real-world testbed environment, the
 experiments have been originally developed by the participating teams in
 AERPAW's digital twin. This public dataset includes data for all
 teams from both the development environment and the real-world
 environment. Names of the teams, their corresponding institutions, and the
 names of the team leads, are as follows.1) Eagles, University of North
 Texas (Lead: Jaya Sravani Mandapaka)2) NYU Wireless, NYU (Lead: Weijie
 Wang)3) Team SunLab, University of Georgia (Lead: Paul Kudyba)4) Team
 Wolfpack, NC State University (Lead: Cole Dickerson)5) Daedalic Wings, NC
 State University (Lead: Baisakhi Chatterjee)
description: # AERPAW Find-a-Rover (AFAR) Challenge in December 2023
 [https://doi.org/10.5061/dryad.18931zd4g](https://doi.org/10.5061/dryad.18931zd4g) ## Description of the data and file structure Each submission from the competitors was executed three times in the development environment, with variations in the UGV's location for each run. There are two folders: "development" and "testbed." The "development" folder includes data on the received signal power and its quality at three different locations. Specifically, the "power_log" file records the received signal power and the corresponding Unix timestamp as a floating-point number, while the "quality_log" file documents the quality of the received power along with the Unix timestamp. In the "testbed" folder, along with "power_log" and "quality_log", there is an additional "log" file. This file contains information about the UAV's location, including longitude, latitude, altitude, and the Unix timestamp, providing a comprehensive dataset for each test scenario. The directory also features angles.mat, detailing the UAV’s roll, yaw, and pitch measurements. ### Contents Main folder: AFAR__2023_SigMF.zip 1. `.\development\loc#` contains the MATLAB post-processing files (`main.m` and `process_txt_CS.m`), along with the power and quality log information represented in `power_log.txt` and `quality_log.txt` in TXT format, respectively. 2. `.\development\loc#\logs` contains the power and quality log information in both SigMF and CSV formats. 3. `.\testbed\loc#`, in addition to the files explained in the development folder (`main.m`, `process_txt_CS.m`, `power_log.txt`, and `quality_log.txt`), contains the function for extracting GPS information. It also contains an additional file, `angles.mat`, which provides the UAV's roll, yaw, and pitch angles. 4. `.\testbed\loc#\logs` contains the power and quality log information in both SigMF and CSV formats, in addition to the GPS log information in both SigMF and CSV formats. #### Raw Data Description 1. **Power Logs**: Each CSV file includes `timestamp` and `power` (in dBm). 2. **GPS Logs**: The generated CSV file has 4 columns. Columns from 1 to 4 represent longitude, latitude, altitude (in meters), and timestamp (in seconds from epoch time), respectively. 3. **Quality Logs**: The generated CSV file has 2 columns. The first column represents the `timestamp`, while the second column represents the quality of measurement. #### Supplementary MATLAB Scripts 1. **`Real_vs_DT.m`**: This script compares *real* testbed flight data with *digital twin* (DT) simulation results. &gt; **Important**: Before running `Real_vs_DT.m`, you **must first run the main entry script** (e.g., `main.m`) inside the corresponding `testbed/loc#` folder. This step processes the raw logs and generates `results_all.mat`, which is required by `Real_vs_DT.m`. 2. **`Roll_Yaw_Pitch.m`**: This standalone script loads the `angles.mat` file and visualizes the UAV’s roll, pitch, and yaw over time. You can point it to any location (e.g., `328/testbed/loc1`) by modifying the `folder` variable. ### Presentation File This presentation (i.e., `Datasets from AFAR Challenge.pptx`) provides an overview of the AFAR Challenge and datasets. It includes: * A description of the scenario and dataset structure (real-world and digital twin). * The role of UAVs and metrics used in evaluation. * File summaries (e.g., `log.csv`, `power_log.txt`, `quality_log.txt`, `angles.mat`). * Use cases such as A2G modeling, antenna gain analysis, and machine learning applications. * References to publications using this dataset. ### Version changes **8 September 2025:** Added `angles.mat`, which contains the UAV’s roll, yaw, and pitch measurements. Additionally, two new MATLAB scripts have been included: `Real_vs_DT.m` and `Roll_Yaw_Pitch.m`. A presentation deck summarizing the AFAR Challenge and the associated datasets has also been added. ### SigMF format This dataset is compatible with SigMF v1.2.0. ### Sharing/Access information NA ## Code/Software Python, MATLAB