Advancements in Machine Learning and AI for Counterdrone Detection and Response
Traditional methods of counterdrone detection and response, while acting as pioneers for the industry, fail to keep up with emerging UAS technologies. This has less to do with the innovation of earlier counter-uncrewed aerial systems (C-UAS) technology than it does with an exponentially in-demand and evolving UAS arms race. With a rapidly growing market, both in terms of consumer-grade and military-ready drones for sale, the only hope that a counter market has to combat malicious drones is by advancing Electronic Warfare (EW) detection and response capabilities through Machine Learning (ML) and Artificial Intelligence (AI) development.
DroneSentry-C2 counterdrone command-and-control system
Why are Counterdrone Defense Measures Important?
Counter-uncrewed systems (C-UxS) provide counterdrone protection measures to varying degrees. Ranging from purely detection systems for threat assessment to disrupt and defeat technology, advancements in the space have given businesses, events, and even nations the ability to respond to rising UAS threats. An example is DroneShield’s innovative AI/ML technology being used in Ukraine’s defense against Russian drone warfare to provide real-time threat analysis and informed countermeasures.
Given the severe and immediate nature of this UAS threat, it is as important as ever to give those on the defensive the chance to have up-to-date and effective threat assessment and strategy implementation competencies.
AI/ML Counterdrone Advancement
With the power of AI/ML, integrated systems can process large amounts of data from multiple sensors to create an accurate and efficient drone detection system. This advancement allows operators, and even the systems themselves, to adapt to scenarios, differentiate between organic (i.e. birds) and inorganic objects (i.e. UxS), track, classify, and identify drones, provide intuitive training, assess the threat, and ultimately create and implement strategies for response and mitigation. After all, emerging technology is starting to advance its Electronic Counter Countermeasures (ECCM), so constant technological innovation and operator management and training is necessary for future advancement.
Weakness of Traditional Counterdrone Solutions
Traditional multi-sensor solutions do not fuse data from all sources. Instead, they collect and display data from multiple sources individually, often with minimal processing methods, such as triangulation. These solutions can also potentially be unreliable, providing inconsistent results with false-positives and false-negatives.
The accuracy of the technology itself is not the only issue. There’s also the mental load, or cognitive burden, on operators. With new and sometimes conflicting systems, both in the UAS and C-UAS space, being put to market globally, the amount of data that an operator needs to keep track of can be overwhelming. That’s where Sensor Fusion fills the gap.
The Answer: Sensor Fusion
Sensor Fusion is the culmination of AI/ML development in the anti-drone defence space. By utilizing the computing and transformational power of AI/ML, Sensor Fusion combines sensor data from multiple and often disparate sources to form a single, easy-to-use, data point. SensorFusionAI allows operators the ability to view a confidence percentage for each detection, and a threat percentage (through ThreatAI) to determine how worrying a drone may be. This ultimately gives operators immediate, actionable, and reliable information with which they can form strategies and deploy countermeasures.
DroneShield’s SensorFusionAI
DroneShield’s SensorFusionAI builds an evolving model that is informed by all inputs over time, leveraging the strengths of each sensor while offsetting their weaknesses through Sensor Fusion and product integration. By focusing the entire process into supporting the decisions of operators, whether they are passive or active countermeasures, SensorFusionAI uses the power of Data Fusion to detect, track, classify, mitigate, and analyze malicious or unauthorized drones.
When it comes to countering drones, the most important factor is adaptation. By using a system that combines sensor data to allow operators the ability to adapt and react in time, lives are saved. Whether this is implemented in warfare, during large events, or the creation of anti-drone airports, the rising and influential role of AI/ML advancement in providing these possibilities cannot be overstated.