Latest Advances in Machine Learning and Artificial Intelligence for Drone Tracking Systems
The modern development in Unmanned Aircraft Systems (UAS) makes it difficult for conventional drone defense systems. Hence, machine learning (ML) and artificial intelligence (AI) are integrated into the drone tracking and defense system.
Machine learning technology is used in drone detection through behavior analysis approach. Depending on the flying object’s speed, size, and trajectory, its intent is identified. Such advanced technology has become the backbone of the military drone defense system. Otherwise, it becomes very challenging for the operator to analyze all the data received from different sensors and make a decision.
In this blog, we will discuss the latest developments in the UAS tracking and defense system using ML and AI.
Why Should You Be Bothered About the Drone Defense System?
A counter-drone defense system is necessary to thwart possible unwanted activities. With the widespread availability of drones, it has become challenging to keep important places like airports and government offices free from drone attacks.
Radar technology is the conventional way to identify any flying objects. However, this conventional way of a drone tracking system can be difficult to identify birds from tiny drones at the same speed. ML and AI come here to fill the gap. Machine learning and artificial intelligence come into play to train the drone detection system to differentiate between a flying drone and a bird (for example).
How is ML/AI Helping Advance the Drone Detection System?
In terms of improving the drone detection system, a subset of ML – deep learning – is transforming the area. Deep learning has improved the drone detection system with real-time analysis. The system can process data in large volumes. So, analyzing data received from different sensors like radar and cameras is a piece of cake for deep learning. It even accurately trains the drone tracking system to identify drones without mistakes.
The most important thing about a drone defense system is to analyze the data in real time and provide necessary inputs to the controller. Such real-time data processing is critical in preventing rogue drone attacks.
Artificial intelligence, along with deep learning, helps the operators in risk analysis. These technologies use images, video, radio frequency, and radar inputs to make an accurate prediction of the unidentified drone in the airspace.
The result is better drone detection in real time, which helps in improving the drone tracking system. The responsible operator can also rely on the real-time drone detection system to make accurate decisions. This way, modern technologies are making way for the automation of the drone detection and alarm system.
How Do ML and AI Help Improve Operators’ Efficiency?
In any occasion- civil or military – decision-making plays a crucial role. The operator has to make that crucial call – whether to engage or leave the object flying in the restricted airspace. Whatever the decision may be, the stake is high. If the tracking system is not accurate, it can falsely detect a bird as a tiny drone. Alternatively, a real drone can be mistaken for a bird, posing a serious risk to civilians.
AI and ML offer a reliable output after analyzing the data received from different sources. It helps the operator make the right call at the right time. So to speak, AI and ML reduce errors in decision-making for the operator.
The AI and ML-powered algorithm for the drone tracking system can analyze hundreds of datasets in real time that would have been difficult to analyze for a human being. In the meantime, the operator can arrange to make a necessary response to track down the rogue drone.
A reliable and well-trained drone detection system can help improve drone detection accuracy. The system can adapt to different scenarios and weather conditions and accurately detect drones. It can analyze the speed, trajectory, size, and overall behavior pattern of a drone to determine if it should be intercepted or not.
Challenges of Conventional Drone Defense Systems
Multi-sensor system merely collects data and presents it before the operator. It does not analyze and synthesize the data received from different sources. Consequently, the operator finds himself in a situation where he has to analyze the inputs from different sensors and make a quick decision.
Under heavy mental pressure, the operator can make the wrong decision. However, the AI and ML-powered drone defense systems reduce pressure on the operator by analyzing the inputs and reducing false alarms.
Conclusion
Conventional drone tracking systems put much mental load on the operator. He had to analyze all the visual, audio, or images of the suspected flying object and determine if it was a drone or something else. However, AI and ML-powered drone detection and tracking systems are more efficient in identifying a drone. Modern systems analyze multiple data in real-time and make a decision.
It reduces false alarms and improves the safety of civilians and other parties in society.
Also Read-AI Music Generators: The Future of Sound or a Passing Trend?
