Erik Unger

Most of the focus is still on CUAS. That makes sense in the near term. But CUxS needs to be front of mind now. If you oversolve or overspend in one domain, the adversary will just adapt in another. We’ve already seen the shift toward unmanned capability on the ground (UGVs) and at sea (USVs, UUVs). Yet most fielded counter capability is still almost entirely focused on what’s in the air. Ukraine continues to show how fast the counter-counter evolves and how decisive the inability to counter unmanned systems can be in shaping outcomes. But treating this as primarily an aerial problem is a mistake. It’s like assuming future fighting will only ever happen on identical terrain, with identical operational constraints. - The open waters of the Pacific. - Rocky terrain. - Dense urban environments. - Dense jungle. - Extreme cold. Unmanned systems will be present in all of them (eventually). They won’t all look the same. They won’t behave the same. This is where multi-domain and cross-domain thinking makes a difference. Each domain will continue to adapt on its own. But the bigger risk comes from coordinated multi-domain strategies and cross-domain autonomous operations. CUAS, by definition, is an incomplete way to frame the problem. At this stage, thinking counter-UxS isn't optionally if you’re building or developing capabilities. Questions that should already be driving design decisions for "counter-UAS" solution providers: - How does the counter adapt across multiple domains? - How feasible is deployment outside the air domain? - How modular is the system inside different architectures and operational constraints? That needs to be the criteria now. Not just optimizing for a single domain, a single scenario, or perfecting one sensor or effector inside a static CUAS stack at the expense of flexibility when the threat changes. Steven Simoni and I touched on this in a conversation on The Drone Ultimatum just a couple months back. A lot has progressed even since then. The clip shared recently captures part of that discussion, and the full interview goes deeper. Always good to catch up with Steve and team. This is a discussion that's going to matter more over the next few years. #DefenseTech #Autonomy #CounterUxS #Robotics

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Survey Review Prize 2026 The Survey Review Prize is a biennial for the best paper submitted for peer review and presentation at the Congress or Working Week of the International Federation of Surveyors (FIG) and submitted by an author who fulfils the FIG definition of a Young Surveyor. It is worth €2000. It has this year been awarded for the paper UAV LiDAR flight planning for high-precision rockfall monitoring in alpine environments: first strategies and preliminary results on Mt. Hochvogel by Leonard Trill, Lukas Raffl and Christoph Holst. Leonard and Lukas are Ph D students at the Technical University of Munich and Christoph is a Professor of Engineering Geology As the abstract notes “This paper outlines a practical workflow for surveying alpine regions that are susceptible to rockfalls using UAV LiDAR. The primary goal is to capture the area of interest as comprehensively as possible while ensuring high geometric resolution and accuracy. The generated point clouds are utilized to detect deformations with high precision, capable of measuring changes within a few centimeters. The resulting findings contribute to alpine monitoring and thus also to future hazard prevention measures to protect people and nature. In this study, we apply our workflow to the rockfall on Mt. Hochvogel, located at the border between Germany and Austria”. With climate change leading to an increase in landslides and rockfalls as a result of the decline in permafrost due to rising temperatures and more frequent weather events, understanding and predicting such natural hazards has become increasingly important and the paper makes a important contribution to this endeavour.  Congratulations to the authors for an outstanding paper. The paper is published by Survey Review as an open access paper at https://lnkd.in/ecye_mv6

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𝙎𝙬𝙞𝙩𝙯𝙚𝙧𝙡𝙖𝙣𝙙’𝙨 𝙏𝙖𝙨𝙠𝙛𝙤𝙧𝙘𝙚 𝘿𝙧𝙤𝙣𝙚𝙨 𝙅𝙪𝙨𝙩 𝙎𝙝𝙤𝙬𝙚𝙙 𝙃𝙤𝙬 𝙩𝙤 𝙒𝙧𝙞𝙩𝙚 𝘿𝙧𝙤𝙣𝙚 𝙍𝙚𝙦𝙪𝙞𝙧𝙚𝙢𝙚𝙣𝙩𝙨 🔍 One of the hardest problems in drone procurement isn’t “which platform?” It’s describing the mission in a way industry can build, test, and price—without turning it into doctrine. Switzerland’s Taskforce Drones did something refreshingly practical: standard operation scenarios + a menu of optional capabilities. 🎯 Four scenario “building blocks” (clear, testable, and procurement-friendly): ▪️ Attack drone: one-way precision strike against ground targets (incl. beyond line of sight), using external target detection. ▪️ Airdrop drone: UAV that releases an effector (not necessarily expendable like a one-way system). ▪️ Ambush: attack drone that can wait concealed on the ground at a choke point, then strike by surprise. ▪️ CUAS interceptor drone: very fast interceptor for kinetic engagement of small UAVs—cuing first, autonomous final approach second. 🧭 What makes it smart (and scalable): ▪️ Range brackets are explicit (e.g., up to 15 km, with optional steps beyond that). ▪️ Environment matters: rural/urban lowlands, low mountain range, high mountains. ▪️ Reality checks: day/night, rain, snow, storms, fog, wind—written as capability conditions, not excuses after trials. ⚙️ The procurement lesson: Stop buying “a drone.” Start buying mission packages with a minimum baseline, then add options like LEGO—effects, targets, ranges, environments. 𝘐𝘧 𝘺𝘰𝘶 𝘤𝘢𝘯’𝘵 𝘸𝘳𝘪𝘵𝘦 𝘪𝘵 𝘢𝘴 𝘢 𝘵𝘦𝘴𝘵𝘢𝘣𝘭𝘦 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰, 𝘺𝘰𝘶’𝘭𝘭 𝘱𝘢𝘺 𝘧𝘰𝘳 𝘪𝘵 𝘢𝘴 𝘢 𝘸𝘪𝘴𝘩. ✅ This is the kind of framing that accelerates market engagement, reduces ambiguity, and makes evaluation defensible. #DefenseInnovation #Drones #LoiteringMunitions #CounterUAS #MilitaryProcurement #RequirementsEngineering

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UAV Navigation: What Works TODAY (not in next 3-5 years).  a few days ago, I wrote about future navigation methods without GPS. We had a great discussion in the comments! Future tech is exciting. But today, we need to fly now. Here are 7 proven methods for commercial & defense drones - short and technical: 1. GNSS Triangulation via 4+ satellites. • Accuracy: 1-3 m (standard), 1-2 cm (RTK) + Global, cheap - Easy to jam/spoof, no indoor Use: Base layer - always have backup. 2. IMU Accelerometers + gyros. Position = double integration. • Rate: 500-2000 Hz | Drift: 1-10 m/min + Autonomous, high rate - Error grows fast Use: Required in every autonomous drone. 3. Optical Flow Downward camera tracks visual features. • Height: 0.3-5 m | Needs texture + Cheap, good for hovering - 2D only, fails on white/water Use: Consumer drones, low-altitude hold. 4. VIO Camera + IMU via EKF/factor graph. • Accuracy: 0.5-2% of path | Needs CPU/GPU + Works without map, 3D - Needs light/texture Use: Gold standard (DJI, Skydio). 5. LiDAR SLAM Laser measures distances + builds 3D map. • Accuracy: 2-10 cm | Range: 10-200 m + Works in dark, precise = Expensive, heavy, rain-sensitive Use: Complex missions, indoor inspection. 6. UWB Anchors + drone. Position via TDoA. • Accuracy: 5-30 cm | Latency: <10 ms + High accuracy, low latency - Needs infrastructure Use: Warehouses, hangars, closed areas. 7. Sensor Fusion EKF/UKF combines 5-10 sensors dynamically. • Rate: 200-1000 Hz | Needs hardware sync + Fault-tolerant, adaptive - Hard to tune Use: The brain. No autonomy without it. Just my IMHO about combos: • Indoors → VIO + UWB + barometer • Urban → GNSS + VIO + barometer • EW/Jamming → IMU + LiDAR + magnetometer • Warehouse → UWB + Optical Flow Key insight from me as CTO of #SpearX: Cheap sensors + good fusion > expensive sensors + bad software. Hardware is 50% of success. The other 50% - algorithms that decide which sensor to trust now. Questions for you: 1. Which method do you use most? 2. Weakest point in modern nav systems? Share your experience in the comments - in engineering, we all learn from each other. PS If you want to know more about any of the navigation methods, ask me in the comments. #CTO #DroneEngineering #Navigation #UAV #SpearX #SensorFusion #VIO #LiDAR #EdgeAI

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