SpearX

We worked quietly. The noise comes later. Today we received our first grant from BRAVE1 for our Ukrainian company, part of SCYTHIA GROUP. This isn't the finish line, not even the halfway point. It's simply confirmation that we're moving in the right direction and building something that matters. Thank you to the team. The strongest results always start with people. Remember this moment. We're just getting started. #WorksProEngineering #ScythiaGroup #GeoComCo #SpearXproject #VisorOptiXproject #Brave1 #Ukraine #Bulgaria #Estonia #DefenseTech #Innovation

SpearX at HEMUS'2026 - Next Big Step!  We are happy to share that the SpearX team will join HEMUS 2026 - biggest exhibitions in of Eastern Europe for defense, security & new technologies.  The event takes place June 3-6 in Plovdiv, Bulgaria. When you visit our booth You will see: - New C-UAS (Counter-Drone) solutions made for real missions - New product launches - EW product & cameras from our partners - Live demos and simple explanations of our technology - Chance to talk about custom solutions for defense, infrastructure protection, public safety, rescue operations & electronic warfare. - This is SpearX's first public exhibition. We are proud to start our journey at HEMUS'2026. This event brings together innovation, industry & national strength - values that match our goal: to build smart, reliable, and scalable defense technology. - If you are a potential partner, investor, integrator or just interested in the future of airspace security - we want to meet you. Find us at HEMUS 2026, Pavilion 2, Booth P3. P.S. Fasten your seatbelts - we are taking off! If you cannot visit the exhibition but want to follow our news and new product launches - subscribe to our page here on LinkedIn and visit www.spearx.eu #SpearX #HEMUS2026 #CUAS #EW #RescueOperations #DroneDefense #CounterUAS #DefenseTech #BulgariaInnovation #DeepTech #Aerospace #SecuritySolutions #FirstExhibition #EngineeringExcellence Book a short meeting with us at HEMUS'2026 in comment.

Why 15 Pixels isn't "low data" - It's an ENGINEERING CHALLENGE. Topic about: Small target detection: Optics, Algorithms & Tradeoffs. In object detection & recognition tasks, often we hear: "The target is too small. We need higher resolution." At SpearX, we see it differently. 15 pixels isn't a data shortage. It's a design constraint that forces better engineering. §  The Reality of Small Targets At range, a drone isn't a detailed object. It's a moving cluster of 10-15 pixels. Traditional CV models fail here. Why? They rely on shape, texture, and context - all of which vanish at distance. §  Motion Over Appearance When pixels are scarce, motion becomes the signal. Optical flow, trajectory consistency, and behavioral patterns matter more than static features. A 15-pixel target moving against background flow is detectable long before it's "recognizable." §  Trade-Off Triangle Optics ↔ Latency ↔ Compute Higher zoom = narrower FOV + slower response. More pixels = heavier models + edge delay. We optimize for the sweet spot: sufficient angular resolution + real-time inference + robust tracking under vibration. How we handle it at SpearX §  Motion-first detection: prioritize kinematic signatures over visual detail. §  Temporal filtering: track across frames, not single shots - errors must not propagate. §  Uncertainty-aware weighting: if vision confidence drops, we lean on trajectory prediction or RF cues. §  Edge-optimized models: quantized, pruned, but mathematically sound. §  Bounded adaptation: like the "Avoidance Limit" concept, we define confidence envelopes - the system knows when it's uncertain and adjusts behavior accordingly You don't need more pixels! You need better signal extraction from fewer pixels! Small targets force us to stop chasing resolution and start engineering for motion, context & certainty. The same principle applies to fleet coordination: precision in time and space matters more than raw data volume. A 15-pixel target tracked with temporal consistency beats a 100-pixel blur with no trajectory. #UAV #ComputerVision #CUAS #SmallTargetDetection #AutonomousSystems #EdgeAI #SensorFusion #SpearX #Engineering #TrajectoryManagement #CTO #CEO #Ukraine #Estonia #Bulgaria #Tracking #TrackingTarget  #Defensetech

"Open architecture CUAS system" Is not a buzzword - It's Survival ! In Defense and Autonomy, "Open Architecture" is often just a marketing checkbox... At SpearX, we treat it as an engineering imperative. See below: The problem with closed systems - You build a great C-UAS system. Great sensors. Great algorithms. Perfect integration. Then: A customer wants to add their preferred radar (just for example). New rules require a different communication protocol. A partner's loitering munition must integrate with your C2. A competitor releases a sensor 10x better than yours. If your system is closed - you lose. You rebuild from scratch. You lose months. You lose the deal. What "open architecture" really means ? - Modularity by design Sensors, guidance, C2, effectors - each part is replaceable. Swap a camera without rewriting guidance. Change an effector without touching detection. - Standardized interfaces Clear, documented APIs. Not just for software - for hardware too. Mounting points, power needs, data formats, time sync. - Third-party compatibility Can you plug in a competitor's sensor? Can your system talk to a partner's C2? If the answer is "only with custom work" - it's not open. - Future-proofing New tech arrives every 12-18 months. Your architecture must absorb it without breaking. How we do it at SpearX: 1. Hardware Abstraction Layer (HAL) Sensors talk to HAL, not directly to algorithms. Swap the sensor - keep the interface. 2. Message-based middleware Components communicate via standard messages (like ROS 2, but hardened for field use). Loose coupling. High flexibility. 3. Plugin architecture New algorithm? New effector? New protocol? Write a plugin. Don't touch the core. 4. Document everything APIs, data schemas, timing needs, power budgets. If it's not documented - it's not open. The real test: Can a third-party engineer integrate their sensor into your system in one-two week without calling your support? If yes: you have open architecture. If no: you have marketing. Why this matters now: The autonomy ecosystem is fragmenting. Specialized sensors. Specialized effectors. Specialized C2. No single company does everything best. Interoperability is the new competitive advantage. At SpearX, we don't try to own the whole stack. We try to own the best integration layer. #OpenArchitecture #ModularSystems #DefenseInnovation #SystemsEngineering #CUAS #Interoperability #SpearX #TechLeadership #SpearX #UAV #DefenseTech #EdgeAI #EUDefense #CTO #CEO #Ukraine #C2 #Sensors #AutonomousSystems

How testing C-UAS without real drones: Simulation, HIL, Digital Twins. Hard truth: You cannot test C-UAS only with real drones. Too expensive. Too slow. Too dangerous. Too hard to repeat. So, how do you verify before field tests? At SpearX, we use a three-level testing pyramid. Level 1: Simulation 85% Why: Infinite scenarios, zero risk. Repeatable. Edge cases on demand. We simulate: -       Realistic physics: wind, turbulence, sensor noise, delays -       Multi-drone scenarios: swarms, coordinated attacks -       Sensor models: EO/IR with distortion, RF with multipath, acoustic noise Tools: Gazebo, AirSim, custom engines. Digital twins of test sites. Key: Domain randomization - every run has different parameters. Prevents overfitting. Level 2: Hardware-in-the-Loop 10% Why: Real hardware has quirks: timing jitter, sensor drift, power spikes. What is HIL: Real flight controller + algorithms talk to simulator. Nothing moves, but everything tests. We test: -       Real-time performance under load -       Integration bugs (driver crashes, packet loss) -       Edge cases: GPS spoofing, wind gusts, sensor failure Setup: Real hardware + simulated sensors + real-time simulator + logging. Level 3: Field Tests 5% Why: Eventually, touch grass. But 99% of bugs are already found. We test: -       Sim-to-real gap: where predictions mismatch reality -       Real-world factors: wind, RF interference, lighting -       Human factors: operator workload, interface clarity Method: Start small → tethered flights → simple scenarios → gradually increase complexity. Metrics: Detection accuracy, tracking stability, interception precision, latency, false positives. Digital Twin Advantage At #SpearX, we maintain digital twins of every vehicle, sensor & test site. After each field test, real data improves the simulator. Next simulation is more realistic. Virtuous cycle: Simulate → Test → Learn → Improve → Repeat Every layer catches different bugs. Skip one - find them in production. At SpearX, 90% of testing happens in simulation. That's how we move fast without breaking things. #Simulation #HIL #DigitalTwin #CUAS #TestingMethodology #SimToReal #SystemsEngineering #SpearX #AutonomousSystems #CTO #CEO #Ukraine #Testing #UAV #DroneSwarm #DefenseTech #EdgeAI #EUDefense #AutonomousSystems

Drone Swarms Are Here. Your Defense System Is Already Too Slow. Three years ago, “drone swarm” sounded like science fiction. Today, it is a daily operational reality. And the basic rules of aerial defense have already changed. Why? reflections on the topic. Traditional air defense was built to track fast, expensive, and rare targets. Swarms change the math completely. Dozens of low-cost, autonomous drones can overwhelm radar capacity, avoid jamming zones, and adapt their flight paths in real time. Lose a third of the group? The swarm simply recalculates and continues the mission. In many cases, the cost of shooting down a swarm is higher than the cost of building it. This is no longer theory. It is happening on real battlefields and critical infrastructure perimeters right now. But here is what most news reports ignore: swarm technology is not only a threat. It is also one of the strongest tools we have to build smarter, faster, and more affordable security. A coordinated group of drones can monitor huge areas, track targets from multiple angles, and share data without a single point of failure. Instead of relying on one expensive interceptor, you get a flexible network that learns, adapts, and scales with the mission. In 2026, three clear trends are shaping this space: - Decentralized control → No single “leader” drone to target or shoot down. - Onboard AI → Real-time decisions made on the device, without waiting for slow or jammed cloud links. - Multi-sensor fusion → Visual, thermal, and radio data combined to reduce false alarms and improve accuracy. Think about border monitoring or power grid protection. A single drone misses shadows, blind spots, and sudden changes. A coordinated swarm covers every angle, shares live video, and flags anomalies before they become incidents. The same logic applies to search-and-rescue, port security, and industrial site monitoring. Dual-use flexibility is the real advantage. Defense budgets are shifting from heavy platforms to distributed networks. The focus is no longer on buying fewer, bigger systems. It is on building resilient, scalable, and intelligent fleets. Companies that understand this shift will lead the market. Those who ignore it will pay the price in security gaps and lost efficiency. The real question is NO longer “Will swarms change security?” The real question is “How will you use them into your operations?” #DroneSwarm #DefenseTech #EdgeAI #EUDefense #AutonomousSystems #SpearX #FutureOfSecurity #UAV #CUAS#MilitaryInnovation #CounterUAS #CTO #CEO #Ukraine #BorderControl #DualUse #CoastGuard #Police #Security #Observation #Reconnaissance 

At SpearX, we face this question daily. When a target changes acceleration & our vehicle has strict limits on power, reaction time & maneuverability, classic controllers start to struggle. We don't just debate theory-we build systems that must work in the real world. So, we tested both paths: strict prediction and adaptive learning. Here is what we learned - see below: MPC (Model Predictive Control) is our cautious strategist. At every step, it calculates moves ahead, optimizing control while respecting hard physical limits: "stay within thrust bounds," "avoid excessive tilt," "compensate for actuator delays." Why we use it: fully transparent, guarantees constraint compliance, robust to disturbances. ! The challenge: needs precise models and serious computing power in real-time. Reinforcement Learning (RL) is our adaptive explorer. It does not solve equations. It learns a "policy" through rewards and penalties in simulation. It finds non-obvious maneuvers, handles complex scenarios, and adapts without manual tuning. Why we explore it: flexible, excels in chaotic or poorly defined situations. ! Risk: "black box" behavior. Hard to explain decisions or guarantee safety outside trained data. The Sim-to-Real Gap: Where We Spend Most of Our Time. A policy that works perfectly in our digital lab often fails in field tests. Unmodeled vibrations, sensor drift, communication lag, mechanical wear-reality is messy. At SpearX, we fight this with domain randomization, live parameter updates, and critically, hard safety layers that physically block the AI from crossing dangerous boundaries. For us, testing is not optional. It is the only bridge between virtual success and physical reliability. Our Engineering Verdict: Stop Choosing. Start Combining. We are not picking "MPC or RL." We are building symbiosis: • RL + MPC: Neural networks handle high-level strategy and uncertainty; classic controllers enforce safe, bounded execution. • Learning-based MPC: Model parameters update live using sensor data-boosting accuracy without losing verifiability. • Safe RL: Learning algorithms wrapped in mathematical barriers that make critical failures physically impossible. Bottom Line: The shift is not about replacing mathematics with AI.  It is about building hybrid loops where learning expands capability, and formal models guarantee predictability. At #SpearX, we believe: fast code means nothing if it ignores physics. Verification must happen before deployment, not after! #ModelPredictiveControl #ReinforcementLearning #Drone #AutonomousSystems #Robotics #DefenseTech  #UAV #CUAS #ControlEngineering #EdgeAI #AI #Engineering #SimToReal #SafeAI #SystemsEngineering #SpearX #CTO #CEO #Ukraine #LWIR #SWIR #SensorFusion

Productive meeting. Important document signed. Strong people in the room. Now execution. Forward, for the security of Europe.

How can a 2-pixel error cause a 50-meter miss? Vision-based guidance is not just "camera sees → system turns". It is a careful dance between how the algorithm "sees" & how the machine physically moves. When a camera replaces radar in the control loop, computer vision stops being a "black box" - it becomes part of the flight physics. How does "camera → steering command" really work? -       The eyes: from image to space A camera is not just video for a human. For the algorithm, each frame is a set of coordinates. But lenses distort the image, vibrations shift the horizon, and targets can be just 5-15 pixels wide. Engineers must teach the system to understand real-world geometry - filtering noise so each pixel matches a real direction in space. -       Time matters: speed over resolution The chain "capture → process → decide → act" takes time. While the algorithm processes one frame, a fast target has already moved. Miss 2–3 frames at high speed, and the system is chasing the past. Good algorithms don't just detect - they predict where the target will be when the command actually executes. -       Scale and optical flow A 1-pixel shift means centimeters up close, but meters at long range. Classic guidance uses a simple idea: if the target stays in the same spot in the image, paths will intersect. But cameras measure angles, while controls adjust linear motion. The system must adapt its response - even without knowing exact distance. -       Filtering noise, trusting wisely Camera data is always noisy. How do we tell a real target maneuver from camera shake or detection error? Smart filters smooth the signal. Too sensitive → the system jerks. Too slow → it misses the turn. Modern systems also estimate confidence: if vision is uncertain (e.g., due to glare), the controller eases its response instead of overreacting. How do engineers connect vision and control? 1️. Remove self-motion - A moving platform makes the whole scene shift. Algorithms subtract the platform's own rotation to isolate the target's true motion. 2️. Predict, don't just react - Instead of reacting to the current pixel position, advanced systems forecast where the target will be when actuators respond. This needs tight time sync between vision and control modules. 3️. Share uncertainty - The vision module doesn't just send "(x, y)". It adds: "I'm 85% sure". If confidence drops, the guidance law automatically becomes more cautious. Key insight: In autonomous guidance, there is no such thing as "just an image". A tiny pixel error, multiplied by speed and system delay, becomes a real-world miss. The modern approach is co-design: vision algorithms are trained with vehicle dynamics in mind & control laws are built to handle vision-specific errors. Honest algorithms tell the system not only "where the target is", but also "how sure I am". #VisionBasedGuidance #ComputerVision #Robotics #AutonomousSystems #Engineering #AI #ControlSystems #EmbeddedAI #TechInnovation #Ukraine #Bulgaria #EU

From Analog Tracking to AI: The Evolution of Visual Guidance Systems Visual seekers did not just get "better cameras." They changed how machines see, predict, and act. Here is a fact-checked technical timeline: 1950s–1960s: The Analog Era First operational IR seekers emerged mid-1950s with PbS (lead sulfide) detectors and spinning reticles. They tracked only the brightest/hottest point-no classification, just "chase the spot." Examples: AIM-9B Sidewinder (1956, first operational IR missile), AIM-9D/G (1960s variants), AGM-62 Walleye (1966, TV-guided bomb, not IR). 1970s–1980s: Digital Transition CCD sensors and early processors enabled digital correlation tracking. Systems stored target templates and tracked shapes, not just heat. Cooled IR (cryogenic MCT detectors) improved sensitivity. Examples: AGM-65A Maverick (1972, EO variant), AIM-9L (1977, first all-aspect IR), AIM-9M (1982, better flare resistance), AGM-65D (1983, IR Maverick). 1990s–2000s: DSP & Predictive Tracking Digital Signal Processors enabled real-time Kalman filtering and edge detection. Uncooled microbolometers replaced heavy cryogenic IR. Adaptive thresholds handled smoke/partial occlusion. Examples: Storm Shadow/SCALP (2002-2003, IIR terminal guidance), Spike ER/MR (1990s-2000s, EO/IR + fiber link), Brimstone (2005, mmWave radar + semi-active laser + IIR). 2010s–2020s: Edge AI & Neural Networks FPGA/NPU chips brought CNNs to the edge for real-time detection, classification, and segmentation. Feature-level multi-sensor fusion. Total loop latency <50ms under strict SWaP constraints. Examples: AIM-9X Block II (2015, high off-boresight + JHMCS helmet cueing), Switchblade 600 (2010s, loitering munition with EO/IR), Lancet (2019+, AI-assisted terminal guidance), SPIKE NLOS (2010s, AI tracking in heavy jamming). 2020s & Beyond: What's Next? Event-based cameras (microsecond response, zero motion blur), neuromorphic chips, uncooled SWIR/MWIR detectors (QWIP, T2SL), self-supervised learning for field adaptation. Quantum IR sensors for ultimate sensitivity. Key Takeaway Guidance systems evolved from "chasing a bright pixel" to "understanding the full scene." Core challenges remain: latency, noise, power, size. We solve them with neural architectures and hardware co-design instead of analog circuits. From our experience in SpearX: Working on EO/IR systems for UAVs, the bottleneck is rarely the sensor itself. It's integration-multi-spectral sync, deterministic edge inference, and keeping the full loop <50ms in field conditions. Optimize one part, system still fails. Balance is everything. #GuidanceSystems #ComputerVision #DefenseTech #EdgeAI #SensorFusion #AerospaceEngineering #CTO #CEO #MachineLearning #RealTimeSystems #IRImaging #SystemsEngineering #SpearX #Ukraine #missiles #rockets