Development of UTM Monitoring System Based on Network Remote ID with Inverted Teardrop Detection Algorithm

Abstract

The new regulation of Remote Identification (Remote ID) established by the FAA is predicted which will stimulate the application of Remote ID in UAS traffic management (UTM). Our research is aimed at the development of a UTM monitoring system based on the network Remote ID and the implementation of a new collision detection algorithm based on an inverted teardrop shape area and dynamic detection size. The newly introduced detection shape area in the UTM system could improve flight safety, increase airspace traffic, and provide a clear depiction of UAVs’ movement direction. The monitoring system consists of Remote ID hardware, a cloud database, and a web-based UTM application that runs on a personal laptop computer. A flight test was conducted involving a human pilot flying a quadcopter UAV to analyze the performance of the system and algorithm. The result found that the developed UTM monitoring system produces a reasonable average delay of around 0.94[Formula: see text]s with a standard deviation of 0.2[Formula: see text]s. The new detection algorithm shows a promising result that produces a larger buffer distance between UAVs compared to the circle shape algorithms.