The Unmanned Aerial Vehicle (UAV) market is predicted to be worth $56.18 billion by 2027. The global commercial UAV market size alone was valued at $13.44 billion in 2020, with a recorded demand of over 689,000 units. With a wide range of applications, the UAV market is expected to grow at 19.9% CAGR from 2020 to 2027; thus, it is important to implement an unmanned traffic management system to minimize the collision risk between unmanned and manned aircraft.
The popularity and increased use cases for UAVs presents a major challenge for existing airspace traffic management (ATM) systems. Current regulations require UAVs to remain within visual line of sight with their operator, severely limiting the operational utility of such vehicles. As a result of UAVs relying on the operator for situational awareness, an operator can only pilot one UAV at a time, hampering the commercial viability of widespread UAV use. Alternatively, surveillance systems using ground radar can provide situational awareness to UAVs, including the presence of other aircraft in the sky. However, these systems also detect false targets (due to precipitation, buildings, ground vehicles, etc.) in the area that makes the airspace appear more cluttered than it is.
When clutter is constantly being generated, the UAVs’ or operators’ inability to distinguish between real and false targets may result in catastrophe. This includes the increased potential for airborne collisions with manned aircraft – particularly when the UAV cannot avoid both the real target and the false targets, and instead must choose between the two.
In addition, the constant maneuvering required to more around these false targets will force UAVs to take long and winding paths to their destinations, resulting in increased energy expenditure (and increasing operator cost), and even premature landings.
Thus, there is a need to identify and remove clutter from the airspace picture so that it becomes an accurate representation of the environmental risks for UAVs, and thus actionable. That is, the airspace data is presented in a concise manner, allowing the UAV to traverse the area in an efficient manner, without compromising its ability to maintain situational awareness provided by the surveillance systems. Additionally, there is a need to address the dynamic nature of clutter in surveillance systems (e.g., road traffic results in more clutter during the day than at night).
Researchers at The Ohio State University, led by Achal Singhal, have developed a patent pending algorithm that addresses these needs. This invention is directed to dynamically removing clutter in traffic management systems, such as those of UTM and UAM.
This new software technology uses data provided by surveillance systems, including data from active radars, and transponders. The algorithm itself is agnostic regarding the source of the data, and additional data types can be added into the software (including those from acoustic sensors or spectrum sensors). This software can then identify objects that are clutter (including those dynamic in nature, such as the development and dissipation of thunderstorms) and remove them to form an accurate, de-cluttered airspace picture. This way, unmanned vehicles will only react to true hazards in the area.
● Removing clutter and noise from airspace surveillance systems
● Providing real-time actionable airspace data for collision avoidance
● Can be run real-time using non-ITAR data from most surveillance systems
● Algorithm is applicable in any surveillance sensor which generates clutter
● Decongests airspace pictures for UAV technology
● Allows operators, or autonomous collision avoidance systems to make better informed decisions regarding their airspace
● Can classify clutter, including those due to weather events or ground traffic
● Simple to use
● Demonstrated effectiveness: removed ~91% of radar clutter during a flight test of the Ohio 33 Smart Corridor