How counter-drone warfare is shaping the research in defensive warfare

Junaid Suhais

The modern battlefield has been irrevocably altered by the proliferation of small, low-cost Unmanned Aerial Systems (UAS), commonly known as drones. Conflicts from the Nagorno-Karabakh war to the ongoing hostilities in Ukraine have demonstrated that these systems can deliver disproportionate strategic effects for a fraction of the cost of traditional military hardware. They have shattered the long-standing economic and operational calculus of air defence. For decades, air defence was a contest between high-value assets: multi-million-dollar fighter jets versus sophisticated, expensive surface-to-air missile (SAM) systems. Today, a nation’s most critical infrastructure or frontline military positions can be threatened by a commercially available quadcopter modified to carry an explosive payload, costing only a few hundred dollars.

This creates a severe cost-asymmetry dilemma. Expending a USD 100,000 interceptor missile to neutralise a USD 1,000 drone is an unsustainable economic model, especially when faced with the prospect of coordinated ‘swarm’ attacks designed to saturate and overwhelm defences. The drone threat is no longer a niche concern but a central challenge to national security, forcing a fundamental rethink of airspace control, layered defence, and technological investment. The global anti-drone market reflects this urgency, projected to grow from approximately USD 4.48 billion in 2025 to USD 14.51 billion by 2030, at a compound annual growth rate (CAGR) of 26.5 per cent.

For India, with its complex and contested borders, dense civilian airspace, and vulnerability to state-sponsored and non-state asymmetric threats, the challenge is particularly acute. The imperative is not merely to acquire counter-drone technology but to develop a comprehensive, integrated, and economically viable national Counter-Unmanned Aerial System (C-UAS) posture.

The C-UAS Kill Chain 

Effective counter-drone operations are not a single ‘silver bullet’ solution, but a layered, integrated system-of-systems approach. This process, often called the ‘kill chain’, involves detection, tracking, identification, and mitigation. Each stage presents unique technical challenges and relies on a diverse set of technologies.

Detection, Tracking and Identification: The first and most critical step is to find the threat. Drones are inherently difficult to detect due to their small size, low Radar Cross Section (RCS), slow speed, and ability to fly at low altitudes, often lost in ground clutter.

Radar Systems: Specialist drone-detection radars are the primary long-range sensors. Unlike conventional air-surveillance radars designed to ignore small, slow targets like birds, C-UAS radars are fine-tuned for this specific target set. Advanced systems utilise micro-doppler technology, which can detect the unique frequency shift caused by a drone’s rotating blades, allowing them to distinguish a drone from a bird, thereby reducing false alarm rates. They offer 360-degree coverage, provide accurate location and altitude data, and can track multiple targets simultaneously, which is crucial against swarms.

Radio Frequency (RF) Analysers: Most drones communicate with their operator via RF links. RF analysers are passive sensors that scan the electromagnetic spectrum to detect these control signals. By matching the signal signature to a library of known drone protocols, they can often identify the drone’s make and model. More advanced systems can even triangulate the position of both the drone and its operator, providing critical intelligence for a response. However, their primary limitation is their inability to detect autonomous drones operating on pre-programmed flight paths or those using non-standard communication links, such as 5G networks or fibre-optic tethers.

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Electro-Optical (EO) and Infrared (IR) Sensors: High-resolution cameras (EO) and thermal imagers (IR) provide visual confirmation of a detected object. They are essential for identifying the threat and assessing its payload. Modern systems increasingly incorporate AI-powered image recognition algorithms to automatically classify a detected object as a drone, reducing operator workload. Their main drawback is a shorter detection range compared to radar and performance degradation in adverse weather conditions like fog or heavy rain. 

Acoustic Sensors: Microphone arrays can detect the unique sound signature produced by a drone’s propellers. While limited to very short ranges (typically under 500 meters) and less effective in noisy urban environments, they serve as valuable gap-fillers, especially for detecting drones hovering or operating close to the ground where other sensors might struggle. 

Neutralising the Threat 

Once a threat is detected and identified, a decision must be made on how to neutralise it. Mitigation techniques are broadly categorised into ‘soft-kill’ (non-kinetic) and ‘hard-kill’ (kinetic) methods, along with emerging directed-energy weapons. 

Soft-Kill/ Non-Kinetic Countermeasures: These methods aim to disable the drone without physically destroying it, minimising collateral damage. 

RF Jamming: The most common soft-kill method involves broadcasting powerful radio signals to disrupt the communication link between the drone and its operator. This can cause the drone to land, return to its home point, or fall out of the sky. The limitation is that jammers can also interfere with friendly communications and are ineffective against autonomous drones. 

GNSS Spoofing/Jamming: This technique involves either blocking or transmitting false GPS/GNSS signals to confuse the drone's navigation system, causing it to drift off course or fail its mission. 

Cyber Takeover: More sophisticated systems attempt to hijack the drone’s control protocol,