NEW DELHI, — India’s indigenous Arudhra radar, developed by the Defence Research and Development Organisation (DRDO) and manufactured by Bharat Electronics Limited (BEL), has reportedly tracked a simulated F-35A stealth fighter during an AI-assisted exercise. Defence reporting published in June 2026 described the virtual scenario as an examination of how India’s Integrated Air Command and Control System (IACCS) could detect and track low-observable aircraft. However, no official DRDO confirmation of the specific simulation has been identified.
Arudhra Radar Specifications
Arudhra is a ground-based, multifunction active phased-array radar designed for aerial surveillance, detection and tracking. Its publicly reported specifications include a maximum instrumented range of 400 km, detection of a target with a 2 m² radar cross-section (RCS) at up to 300 km, and an altitude coverage of approximately 100 metres to 30 kilometres. It uses electronic beam steering in azimuth and elevation to measure a target’s range, direction, altitude and radial velocity. Its co-located Identification Friend or Foe (IFF) system helps identify cooperating aircraft.
How Could Arudhra Detect a Stealth Target?
Stealth aircraft such as the American F-35 Lightning II use specialised shaping, internal weapons carriage and radar-absorbing materials to reduce radar reflections. Stealth does not make an aircraft completely invisible; it reduces the radar energy returned to a receiver, making detection more difficult.
Arudhra transmits radio waves and processes the small amount of energy reflected by an aircraft. Its phased-array antenna, electronic beam steering and signal-processing capabilities can help detect targets and maintain tracks under suitable conditions. Actual performance depends on the target’s RCS, viewing angle, radar frequency, noise, interference and processing thresholds.
The radar’s 400 km instrumented range does not mean it can detect an F-35 at that distance. Its reported 300 km detection range applies to a specified target size, not automatically to stealth aircraft.
Radar Cross-Section and Detection Range
In a simplified radar model, detection range varies approximately with the fourth root of the target’s RCS, assuming other conditions remain unchanged.
For example, if a radar detects a target with a 2 m² RCS at 300 km, a hypothetical target with a 0.1 m² RCS would have an estimated detection range of approximately 142 km under the same assumptions.
This is an illustrative calculation, not an estimate of Arudhra’s actual range against the F-35. The aircraft’s operational RCS is not publicly established and varies with viewing angle, frequency and configuration.
How Could the AI-Assisted IACCS Simulation Work?
The reported simulation could examine how Arudhra’s radar-processing functions and IACCS handle a virtual stealth target. The exact implementation and inputs used in the reported exercise have not been publicly established.
A technically plausible simulation could involve four stages:
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Arudhra radar model: Simulates radar transmissions, target reflections, background noise and detection limits.
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Simulated target and radar processing: Models an F-35-like aircraft’s assumed RCS and flight path, then tests whether the simulated radar returns produce a detection and a usable track.
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AI-assisted analysis: Analyses noisy measurements, estimates target movement and assesses track quality. AI may help distinguish weak signals from noise, but it can also produce false detections.
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IACCS network simulation: Tests how radar tracks are shared, correlated with other sensor reports and presented to air-defence operators.
This is a technically plausible simulation architecture, not a confirmed description of the system DRDO actually tested.
The result depends on how the virtual target is represented. A predefined target track means the software already knows the aircraft’s simulated position, so successful tracking may primarily demonstrate the simulation and network functions. Synthetic radar returns provide a more meaningful detection test if they realistically model the target’s RCS, flight path and noise. Trials using physical radar hardware and real measurements against representative targets provide stronger evidence of actual radar performance.
AI cannot recover information that is completely absent from the measurements. Its effectiveness depends on the quality of the sensor data and the accuracy of the underlying models.
How Could IACCS Improve Stealth-Target Tracking?
India’s Integrated Air Command and Control System (IACCS) combines available air-surveillance information to support a broader air picture. Reports from multiple compatible radar and passive sensors could help establish a more reliable track if they independently obtain useful measurements of the same aircraft.
However, networking does not guarantee detection if no sensor provides usable information about the target. The significance of the reported simulation depends on whether it used a predefined virtual track, synthetic radar returns or measurements from physical radar hardware.
Does Tracking Mean Arudhra Can Lock Onto an F-35?
Not necessarily. Detection, tracking and weapon engagement are separate capabilities. Detection identifies a possible target, while tracking estimates its position and movement over time. A weapon engagement may require a more accurate and timely fire-control-quality track, depending on the weapon system.
A simulation demonstrating a track against a virtual stealth target would not, by itself, prove that Arudhra can provide the information required to engage an operational F-35.
What the Simulation Means for India’s Air Defence
The reported exercise describes a technically plausible way to evaluate Arudhra’s phased-array radar and tracking capabilities against a low-observable target model. AI-assisted processing and IACCS integration could help assess how weak radar detections are handled across an air-defence network.
However, there is no verified basis to claim that Arudhra has demonstrated detection or tracking of a real F-35 at 300–400 km. Confirming such a capability would require reliable details of the test methodology, target radar signature, measured detection range and supporting live-test results.
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