Preserving capacity, General Tom Lawson, Chief of the Defence Staff, Keys to Canadian SAR
Issue link: http://vanguardcanada.uberflip.com/i/1525681
• Challenges: Radar can struggle with small drones due to their low radar cross-section. 2. Lidar-Based Systems • Principle: Lidar uses laser pulses to mea- sure distances and create 3D maps of the environment. • Advancements: Solid-State Lidars are compact, reliable, and capable of de- tecting small drones. Multi-Wavelength Lidars enhance accuracy by analyzing different wavelengths. • Challenges: Limited range and suscepti- bility to atmospheric conditions. 3. Acoustic Sensors • Principle: Acoustic sensors detect drone propeller noise or other acoustic signa- tures. • Advancements: The use of Microphone Arrays now enables triangulation for precise localization. Machine Learn- ing-Based Classification distinguishes drones from other ambient sounds. • Challenges: Noise interference, false positives, and range limitations. 4. Radio Frequency Detection • Principle: RF detectors identify the communi- cation signals between a drone and its controller. • Advancements: Enhanced sen- sitivity and spectrum analysis capa- bilities, coupled with AI and ML for accurate and real-time identification of drone signals among RF noise. • Challenges: Limited to detecting drones that are actively transmitting signals. Cannot detect autonomous drones that do not rely on RF communication. As drones become integral to various industries, these technologies will shape their future, whether it is countering rogue drones, enabling swarms, navigat- ing autonomously, optimizing energy use, or ensuring secure communication, innovation continues to propel the drone ecosystem forward. To continue develop- ments, experts emphasize the need for collaboration to maximize the interop- erability of technologies. As drone tech- nology continues to evolve, it will shape a safer and more efficient airspace. Role of Machine Learning and Artificial Intelligence Recent advances in drone detection owe much to the integration of Machine Learn- ing (ML) and Artificial Intelligence (AI) technologies. These innovations represent a paradigm shift in security systems, partic- ularly in the identification and mitigation of drone-related threats. At the heart of this progress lies the utili- zation of ML models, which are trained on labeled datasets containing both drone and non-drone imagery. Through this process, F E AT U R E www.vanguardcanada.com AUGUST/SEPTEMBER 2024 13