Southeast Asia’s Counter-drone Efforts |
Asia Defense | Security | Southeast Asia
Southeast Asia’s Counter-drone Efforts
Southeast Asian countries are taking steps to counter drone threats. What’s really needed is a multilayered defense network.
Lessons from the wars in Russia-Ukraine and the Middle East have led Southeast Asian countries to rapidly accelerate efforts to invest in and adopt counter-drone systems.
Malaysia recently unveiled a locally developed interceptor drone, known as “The Ghost.”
Last year, Singapore announced that every recruit entering basic military training will learn how to operate drones and counter drone threats. It also established the Singapore Armed Forces Counter-Unmanned Aerial System (UAS) Development and Operations group.
Indonesia is pursuing defense collaborations with China Aerospace Long-March International Trade Co Ltd (ALIT), and this potential partnership could focus on drones.
Cambodia used fiber-optic drones against Thailand in the border clashes last year. Following that conflict, Thailand set up an unmanned aerial system (UAS) warfare center and a UAS battalion, tasked with the responsibility to direct, plan, control, oversee and integrate the army’s drone operations.
This increased interest in counter-drone technology was also reflected at the Defense Services Asia (DSA) and Milipol TechX (MTX) exhibitions, which concluded in Kuala Lumpur and Singapore, respectively, in April.
While training personnel and investing in hardware, such as sensors and jammers, are important, lessons from the Russia-Ukraine war, the Middle East, and the conflict between Thailand and Cambodia highlight a more critical need: a multilayered approach to counter drone threats.
To effectively counter drones or drone swarms, Southeast Asian countries should consider adopting a defense framework operating across multiple layers.
The first layer of defense involves drone detection. This can be achieved by deploying a combination of systems, including sensors (radio frequency [RF], acoustics, and radar) and cameras (electro-optical and infrared).
Once a drone is detected, the focus shifts to identification. Artificial intelligence (AI) systems, such as AI-based decision-support systems (AI DSS), can be used to distinguish between friendly and hostile drones. AI DSS, such as Lavender and Maven Smart System, are primarily known for their reported use in target identification during armed conflict like those in Gaza and Iran. However, these systems can also be employed to distinguish drones, specifically by integrating and fusing data from sensors and cameras, and analyzing RF signals and other data points, and thereby assisting militaries in identifying drones. These systems may also help to track and prioritize which drones ought to be neutralized first, based on factors such as distance and flight time.
The next layer of defense concerns mitigating drone threats via non-kinetic measures. A primary non-kinetic measure is jamming, such as Global Navigation Satellite System (GNSS) jamming. GNSS jamming entails broadcasting powerful radio signals on the same frequency bands used by GNSS satellites, which causes signal loss and results in drones losing the ability to calculate accurate positions or time.
Another non-kinetic approach is spoofing, which involves sending fake GPS signals, thereby misleading drones into computing incorrect positions. Notably, some spoofed Russian drones were reportedly “redirected” either to their points of origin or into Belarusian territory.
However, both jamming and spoofing can cause significant civilian impacts, such as increasing aviation risks and disrupting shipping and telecommunications. To mitigate these impacts, cyber takeover of drones may also be considered. This can be achieved through multiple methods, including hijacking a drone’s control signals and forcing the drone to fly to a........