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Advantages
The automatic target recognition and tracking platform is designed to hit military vehicles and infantry in the environment, which has different electronic warfare equipment, in the cases when the control signals exchange and video transmission are not possible at the final phase of the flight (500-1000m distance to the target).
ㅤㅤㅤㅤ FPVs, aircraft type UAVs, loitering munitions, cruise missiles, guided bombs
ㅤ - Reverse engineering protection implemented specifically for the case when the drone is captured by the adversary - Per-device license management
ㅤ Over 90% success rate in environments with electronic warfare equipment when the tracking starts at 500 meters distance
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ㅤ Can be deployed by unexperienced UAV pilots by turning on the tracker after take off and adjusting the tracking point during the flight
ㅤ Reference hardware cost is as low as $200 per unit (software license excluded)
ㅤ with different flight controllers and autopilots: - F405, H743, others - Ardupilot, Betaflight, iNav
ㅤ Software can be ported to any hardware platform, which meets performance requirements
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Carriers
The system can be mounted on various carriers:
- FPV drones 7", 10", and 13"
- Aircraft-type UAVs
- Loitering munitions similar to Russia’s “Lancet” drones
- Cruise missiles / guided bombs
The system can be mounted on various carriers:
- "Khrushch" aircraft type UAVs
- Variety of 7", 10"and 13" FPV drones
The system can be mounted on various carriers:
- Ardupilot
- Betaflight
- iNav
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Specifications
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Target capturing and tracking distance: 8mm lens / 50° FOV: up to 1000m
Target speed: depends on carrier drone capabilities. For a basic 10" drone, target speed can be up to 60km/h for both chase and head-on approaches.
Max crosswind: 12kt (6 m/s)
Tracker FPS: 30
Training dataset: proprietary dataset of 150,000+ labeled images
Tracker precision (on training dataset): 0.95
Detection models:
- military vehicles (tanks, personnel carriers, SAMs, trucks)
- military and civilian cars, vans, trucks
- flying targets
- infantry
Detector performance: 10 FPS
Image resolution: 640x480 (zoomed image area: 1440x1080)
Weight: <150g including camera hardware and lens
Gimbal support: 1, 2 axis
Pilot User
ㅤ Interface:
State:
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Field testing videos
Click below to watch the videos
The system can be ported to a different hardware platform within 6-9 weeks.
Adaptation of the existing HW to a new UAV model: 3-4 weeks for setup and field testing
Hardware platform
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Raspberry Pi 4b
RPi Global Shutter Camera
Coral USB stick
(for target detection)
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target detector
For our target detector training, we've collected a dataset with 200K images (25+ hours of video) and labeled it with 12 different classes of targets (Person, BMP, Tank, Truck, ADS, MLRS, Artillery etc.).
The detector can be used to ensure the target hit in the case when for a substantial period of time the line-of-sight has been lost or the target is hidden in smoke.
Even without a detector, our computer vision algorithms and logic incorporated in the tracker module can still keep hold of the target in case of brief line-of-sight losses.
Currently we develop a fully autonomous scenario where the drone autonomously reaches a geo zone specified by the operator at the start of the mission, searches for the target according to the priority and hits it, with the assumption that there is no GPS and no connection between the drone and the operator.
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Control and tracking algorithms are being tested in flight simulators (Uncrashed, XPlane, AirSim, PyBullet) because they allow:
- Specifying UAV parameters and performing initial PIDs tuning before field testing
- Algorithms testing for the scenarios when the targets are moving
- Algorithms testing in different wind conditions
Simulation
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The platform development is being done in close cooperation with the 48th combat unit of AFU, who developed the aircraft type UAV "Khrushch", and with a large drone pilot school.
Team
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Founder
20+ years in IT. Successful due-diligence process and exit with the unicorn company in 2018.
Built and managed R&D centers in Ukraine with a staff of 1000+ team members. Aviation geek, holder of CPL(Aircraft) pilot license.
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Computer Vision Engineer
Technical geek, expert in tracker and detector algorithms, edge-optimized neural network architectures.
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Embedded Engineer
Expert in full-cycle SW/HW development and UAV control algorithms.
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Data Processing Engineer
Expert in building and organizing datasets, semi-automatic annotation, as well as simulators (Uncrashed, XPlane, AirSim, PyBullet etc.).
Q2'2023 project started Q4'2023 first prototype for aircraft type UAV, test flights on "Khrushch" Q1'2024 first FPV prototype, first 7" drone flights Q4'2024 control algorithm finalized, 10" UAVs tests Q3'2026 First tests with the target detector. Start of the autonomous scenario development Q4'2026 autonomous scenario is ready in simulator Q2'2024 software implementation for flight simulators Q3'2025 extensive tests on 10" and 13" UAVs at military training areas Q1'2026 tests of autonomous scenario at military training areas Q2'2026 manufacturer integrarion & battlefield tests
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