[ COUNTER-UAS TRACKING NETWORK ]
Track first.Mount anything.
Cheap drones are draining air defence of interceptors that cost hundreds of thousands of dollars. Killswitch is the tracking and coordination layer that finds each drone, picks the cheapest way to defeat it, and puts a human on every decision.
[ Concept render // simulated engagement ]
PHASE: SOFTWARE_MVP
- Detector latency
- 41 ms[MEASURED]
- Tracking error
- 0.11°[SIM]
- Automated tests
- 235 + 34[MEASURED]
Fixednode.
[ ROOFTOP TRACKING NODE // ~4 KM REACH ][TARGET][ Concept render // simulated engagement ]
Mobilenode.
[ SAME NODE ON A VEHICLE // OWN 24 V POWER ][TARGET][ Concept render // simulated engagement ]
[ THE PROBLEM // COST EXCHANGE ]
Air defence is losing the cost exchange.
01 // Cost disparity
Interceptors costing hundreds of thousands to millions of dollars are fired at drones that cost hundreds to a few thousand.
02 // Magazine depth
Launchers run dry. Swarms don't.
03 // Human bottleneck
One operator per weapon can't keep up with a saturation attack.
04 // Monolithic systems
Each counter-drone system ships as one closed box with one weapon. You can't add a cheaper effector without buying a new system.
[ HOW IT WORKS // THE NETWORK ]
One network, five steps.
One tracking network. Any effector. A human authorises every shot.
Cue
Radar, RF or acoustic sensors flag a contact. These are existing sensors; we integrate with them.
Track
The nearest Killswitch nodes slew to the contact. Wide camera, zoom camera, AI detector, predictive tracking. Two nodes on one drone give its 3D position.[Triangulation: target]
Decide
The network classifies every drone, ranks threats by what is heading for people first, and proposes the cheapest effector for each.
Authorise
Human in the loop
An operator approves, edits or rejects the plan. Nothing fires without a human. One operator for about ten nodes.[Target]
Engage and assess
The node holds aim while the effector works, then confirms the result or re-queues the target.
Node type // Fixed
Fixed node.
A rooftop tracking node. A network of about 30 to 40 covers Singapore at ~4 km reach.[Target, modelled placement]
Node type // Mobile
Mobile node.
The same node on a vehicle, with its own 24 V power, that drives to where the threat is.[Target]
[ EFFECTORS // BRING YOUR OWN INTERCEPTOR ]
Bring your own interceptor.
The tracker is the product. The weapon is a module.
Small quadcopters, short range, clear air
- Proposed effector
- Laser module, 5 kW-class[Target], 3–4 km reach[Target]
- Why
- Cost per shot is electricity; no debris from a missile.
Shahed-class fixed-wing, long range, or rain and fog
- Proposed effector
- Tube-launched interceptor drones, ~US$2,000 class, ~25 km reach[Cited]
- Why
- All-weather; far cheaper than a missile.
High-tier threats
- Proposed effector
- The defender's existing guns or missiles
- Why
- Saved for what only they can stop.
ReDome: our own concept for a reusable interceptor that returns to base on a miss. Concept stage, not built.[Concept]
Interceptor cost and range refer to Sting-class interceptor drones as reported by Al Jazeera (4 May 2026). Killswitch does not build interceptors or high-power lasers. Effectors are supplied and operated by the defender at licensed sites.
[ SAFETY // HUMAN AUTHORITY ]
Designed so a fault makes it stop, not fire.
- 01
The node is the safety authority. Limits, watchdog and e-stop live on the microcontroller. The host can't override them.
[Built] - 02
Nothing moves until an operator arms it.
[Built] - 03
Detection is not authority. A single false detection can never move the head; only a confirmed lock can.
[Built] - 04
Silence means stop. No heartbeat for 500 ms puts the node in fail-safe.
[Built; verified in simulation] - 05
Every engagement needs operator authorisation, and every proposal, approval and override is logged.
[ PROGRESS // WHAT'S REAL TODAY ]
What we've built, and how we know.
Detector latency
41 ms
[MEASURED]Per frame: drone detector on an Apple M1 Pro laptop CPU.
Tracking error
0.11°
[SIM]Mean error on a 20°/s crossing target, with 80 ms camera lag and miscalibrated servos. Real firmware logic.
Automated tests
235 + 34
[MEASURED]235 automated tests plus 34 firmware test cases. No hardware needed.
Deadman cut-off
254 ms
[MEASURED, SEP 2026]Deadman response, with a 2,002 ms burn ceiling, on the hackathon rig.
Firmware interlocks
13/13
[MEASURED, SDTH RIG FIRMWARE V2]Interlock checks passed on the SDTH rig, firmware v2. Not re-run on later firmware.
ATAK-CIV tracks
Live
[DEMONSTRATED, SDTH]Live tracks sent to ATAK-CIV from a real node plus simulated nodes.
Built vs planned.
- Single-node tracking software, lock gate, safety nodeBuilt, verified in simulation
- Physical velocity tracking rigParts in hand, assembly under way
- Zoom camera, XY head, vehicle nodeNext phase
- Multi-node triangulation and effector allocationPlanned
- Laser and interceptor integrationWith defence partners, at licensed ranges
[ ROADMAP ]
From bench rig to partner pilot.
Phase 01 // Current
Software MVP
Tracking software and bench rig; tracking a target through 360°.[Target]
Phase 02
Hardware MVP
Zoom camera, XY head and a vehicle-mounted node; long-range tracking trials.
Phase 03
Pilot
A second node, networked tracking, and a trial with a defence partner.
[ SIMULATION & EFFECT ]
Imbalance over vaporisation.
A 5 kW-class beam[Target] heats a weak structural point until it warps: 5.2 s to effect on a Shahed-class airframe[SIM, transient thermal model]. Not field-tested.
The model
Shahed-class
Transient thermal simulation of a Shahed-class airframe.
The beam
5 kW-class[Target]
Heat concentrated on a weak structural point, not the whole airframe.
Time-to-effect
5.2 s[SIM]
The composite spar warps in the model. Not field-tested.
[ CONCEPT TEST CASES ]
Three concept cases. None measured yet.
Where a 5 kW-class beam[Target] would aim. Only the spar case has a figure, from the thermal model; none of this is a field result.
Polymer rotor blade delamination
- Thermal target area
- Polymer rotor blades, near the hub
- Mechanism
- Rapid thermal degradation delaminates the blade's polymer layers.
- Failure mode
- Lift loss on one arm; the drone can't hold attitude and descends.
[ Simulation // prototype ] Watch the network allocate effectors against a simulated swarm
Automatic allocation. Operator approval not modelled.
[ OUR TEAM ]
Built by engineers from NTU, NUS and industry.

Rohit Panda
AI Engineer
Tracking software
MSc, NTU

Aadith Yadav Govindarajan
Material Science Engineer
Thermal simulation and rig integration
I-FIM, SafeRail.AI

Thejus Aravind
Mechanical Engineer (aeronautical)
Gimbal and mounts
NUS Mars Rover

Evan Tok
Electrical Power Engineer
Power and safety electronics
Siemens Energy
[ BRIEFING // REQUEST ACCESS ]
Request a briefing.
For defence organisations, primes, investors and grant evaluators.
[ Opening soon ]
Briefing requests open soon. Contact us at pandarohit05@gmail.com.
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