Maxym Prasolov, CGO at Bavovna AI, will speak at Battlefield 2027 next week in Washington, D.C., held in parallel with AUSA.

Battlefield 2027 brings together innovators, defense leaders, and technologists shaping the future of conflict. The event focuses on emerging dual-use technologies, autonomy, AI systems, and new strategies inspired by lessons from today’s active warzones — with special attention to the Ukrainian experience and its global impact.

He’ll join a panel alongside:
• Chad McCoy — Co-Founder & CSO, Firestorm
• Brian Streem — Co-Founder & CEO, Vermeer
• Samuel Bendett — Advisor, CNA
• Moderator: Colin Demarest — Future of Defense Reporter, Axios Emerging Defense

The discussion will explore how lessons from #Ukraine are reshaping modern warfare and what technologies are coming next.

Join us — DM Battlefield 2027 or Firestorm to secure your spot.

If you manage a facility where airspace matters, you’ve likely faced a new kind of nuisance: unauthorized drone traffic. UAV sightings are on the rise around airports, industrial facilities, stadiums, and border zones. Some are just loitering hobbyists. Others may be bad actors intent on smuggling, surveillance, or targeted disruption.

To curtail drone instructions, you need the right counter-UAV technology. But confusion often arises between drone detectors and drone jammers. Here’s a quick primer from our team.

What’s a Drone Detector?

An electronic drone detector tracks, identifies, and classifies unmanned aerial vehicles within defined airspace by analyzing different signals they emit. Think of it as an “airspace burglar alarm”.  Unlike an anti-drone system, detectors don’t try to neutralize the detected asset, but only alert you about a buzzing copter. Typically, you’ll get data on what kind of drone is flying, where it is heading, and whether it is being actively controlled or flying autonomously.

A standard drone detector system may include: 

• Radio frequency scanner, which detects and analyzes drone communication
• Radar tracking, configured for low altitude, small-size aircraft detection 
• Acoustic sensors, attuned to the sound signature of drone propellers
• Optical and thermal cameras for visual or infrared confirmation for operators

The most affordable drone detectors (think handheld devices like Dronetag RIDER) only ID drones by RF signatures. More complex systems like DedroneRapidResponse combine several sensor inputs — RF, PTZ camera feed, and radar — plus use AI for built-in noise filtering and enhanced image processing. 

Strengths: Detectors give early warning and situational awareness without interfering with other systems. They are also legal to use in most regions, unlike jammers. 

Limitations: They don’t prevent unauthorized UAVs from breaching your perimeter. And their performance isn’t always reliable. In dense urban areas with lots of signal clutter, lower-end systems may miss the mark. 

When to Use a Drone Detector

• Stadiums, airports, prisons, and corporate campuses that need to spot drones but can’t risk illegal countermeasures
  
• Security teams that want logs of airspace violations for investigations
  
• Organizations aiming to comply with airspace safety regulations while staying non-intrusive
  

What’s a Drone Jammer? 

A drone jammer aims to disrupt communication (RF) and/or navigation signals (GPS/GNSS) most UAVs rely with the following techniques: 

• Radio frequency interference, aimed at serving command and data feeds between the drone and its operator. 

• GNSS/GPS jamming techniques to block satellite positioning signals, causing the drone to lose navigation.
  
• Spoofing, to mislead the drone’s navigation system with false signals, redirecting it to a different location or forcing it into a fail-safe mode.

Unlike a detector, this counter-drone technology actively neutralizes rogue UAVs. Although their range and effectiveness varies a lot based on the antenna setup, frequency coverage, output power, and the countermeasures built into the drone itself. For instance, with Bavovna’s AI hybrid navigation kit, drones can safely cruise without any reliance on maps or GPS. 

Strengths: Jammers and spoofers come at every price point (with the cheapest costing just $150). They can quickly curtail unauthorized drone traffic via forced landing or return-to-home, or full loss of control.  

Limitations: In many jurisdictions, drone jammers are out of reach legally for civilians. Advanced drones with autonomous navigation modes may also resist jamming, making it only one piece of a broader counter-UAV strategy. Detection, however, is fair game and often the smarter first move.

When to Use a Drone Jammer

• ISR, military and defense ops, where neutralizing hostile drones is mission-critical
  
• Critical assets and infrastructure in active conflict zones
  
• VIP or convoy protection in regions where legal exemptions allow counter-UAV systems
  

Drone Detector vs. Jammer: Key Differences

To sum up, here’s a simple breakdown:

Feature	Drone detector	Drone jammer
Function	Identifies and tracks	Disrupts and neutralizes
Approach	Passive, information-based	Active, interference-based
Safety	Non-intrusive	Risk of collateral disruption
Legality	Legal in most regions	Mostly restricted to military/government

The Bottom Line

In practice, detection and jamming are two sides of a layered defense. Detectors provide the intelligence: what’s in the air, where it’s headed, and how it behaves. Jammers (or other counter-UAV tools like net-guns, interceptor drones, or directed energy systems) provide the action when a threat is confirmed.

Without detection, you’re blind. Without countermeasures, you’re toothless. But together, you get a defense strategy that works.

When ground towers go dark or satellites can’t keep up with terrain or interference, airborne relays fill the gap. RC UAVs can be deployed within minutes to extend line-of-sight networks across blocked or hostile terrain.

In practice, communication ranges vary. Small UAVs can hold communication links at 5-10 km on unlicensed 900 MHz bands, going toward 20 km with high-gain antennas and clear Fresnel zones. With specialized SDR platforms or relay chains, you can extend the range beyond 30 km. But at the expense of power, payload, and regulatory compliance.

For operators, these relays buy precious minutes of connectivity in EW-contested airspace. For emergency responders, they connect isolated teams when every second counts.

Technology That Makes Airborne Radio Relays Work

Turning a UAV into a resilient comms node requires more than bolting on an antenna. Each layer below addresses different failure points — interference, drift, weather, or deliberate jamming.

1. Precision Hovering Without GNSS

For a UAV acting as a comms node, stability is non-negotiable. Bavovna’s Airtower Mode

enables sub-0.5% positioning error without relying on satellites. Using a combination of 

real-time sensor fusion and pre-trained AI models, our solution keeps the drone operational 

even in jammed or spoofed environments, ensuring the relay antenna maintains its line-of-sight coverage.

Conventional inertial navigation systems’ drifts accumulate fast, especially under jamming.

Bavovna’s Hybrid INS solves this with AI trained on chaotic, real-world flight paths. During recent tests, the Aurelia X6 Max completed 30+ kilometers of unpredictable maneuvers before returning home with just a 4.2-meter error — a 99.99% accuracy rate. That level of reliability prevents a comms relay from slowly drifting off course and losing coverage.

2. EMI-Schielding 

Electronic shielding matters, but materials matter too. Carbon composites with conductive doping help at lower frequencies but don’t block all bands; aluminum or copper foils often perform better at higher GHz ranges. Enclosures must be grounded and aperture-controlled to avoid leakage.

Beyond shielding, spread-spectrum and fast-hopping techniques make relays harder to jam. FHSS can skip across channels faster than most spot jammers can track, though wideband barrage jamming can still swamp the band. 

3. Adaptive Antenna Systems

Directional patch or helical antennas remain the workhorses for airborne relays. They allow operators to reduce dead zones, especially in mountainous or urban terrain, and gain some extra agility. 

For instance, mechanical dynamic helical antennas can be tuned to real-time flight parameters like velocity, attitude, and Doppler shifts for maximum resonance. This reduces 

signal fading during banking or yawing maneuvers and improves RSSI stability by about 40%, according to research. But the size and of dynamic antennas power draw limit on small UAVs. 

4. Mesh Networking Protocols

A single radio relay is already a great help, but networks have greater resilience. RC UAVs can also be configured as airborne mesh networks to create decentralized communication grids. So if one node is down, you still have connectivity. 

Mobile Ad Hoc Networks(MANET) and Flying Ad Hoc Networks(FANET) protocols let you configure multiple UAVs into self-healing meshes. Both of them are optimized for high mobility and rapid topology changes. The downsides, however, are high-mobility routing overhead and per-hop bandwidth loss (often 30–50%). So engineering choices hinge on what’s more critical: coverage, latency, or throughput.

To Conclude 

Airborne RC UAV relays are not a silver bullet, but they are a proven way to buy connectivity when towers are down and satellites are outmatched. With the right combo of precision navigation, shielding, adaptive antennas, and mesh protocols, UAVs can hold networks together in the toughest conditions.

If your mission demands uninterrupted connectivity, contact Bavovna’s team to learn more about autonomous drone navigation solutions.  

Keeping a drone in the air used to be the job of a steady pilot thumb. Today, it’s the work of silicon. Modern flight controllers (FCs)  do a lot of the ‘hard-lifting’ for pilots, automating tedious manual tasks like stabilization, obstacle avoidance, and fully autonomous flying, thanks to advanced technologies. 

What a Flight Controller Really Does?

A flight controller is a circuit board, placed inside every drone, that processes drone movements and user inputs, using a combination of sensors and software. Its capabilities include: 

• Sensor data operationalization from INS, IMU, and GPS (when available) 
• Motor control, based on processed sensor data about flight conditions 
• Altitude and orientation management, based on operator commands 
• Automated sequence performance (e.g., landing, takeoff, return to home) 
• Obstacle detection, interference management, and auto-flight stabilization 

Without this constant background work, UAVs would drift, wobble, or crash when conditions get rough. 

Autonomy Technology in Modern Flight Controllers 

Autonomy comes from a stack of technologies working in concert inside the flight controller. Edge computing brings processing power onboard. Sensor fusion blends multiple data streams into a coherent flight picture. Machine learning adds adaptability in unpredictable conditions. Mesh networking connects drones into resilient, cooperative fleets. Together, these layers move UAVs from manual control to full autonomous capability.

Edge Computing 

Modern flight controllers come pre-equipped with ARM-based system-on-chip (SoCs) like STM32H7 or NVIDIA Jetson Nano/Orin Nano. SoCs combine GPU, NPU, memory, and I/O interfaces, allowing the drone to run tasks locally instead of sending data back to the ground control station. 

Benefits include:

• Low-latency control loops at >400 Hz for precise motor actuation
  
• Onboard CV algorithms for obstacle detection or target recognition without requiring uplink bandwidth
  
• Energy optimization algorithms running in real time to extend endurance. This makes the UAV less dependent on external processing and more resilient in GNSS/GPS-denied operations.

Sensor Fusion

SoCs also enable another cool feature in flight controllers: multi-sensor data integration. RAW IMU data (e.g., from accelerometer and gyroscope) is drift-prone. And magnometers are sensitive to interferences.  To compensate for individual sensor deficiencies, you can deploy Extended or Unscented Kalman Filters (EKF/UKF) on the drone. 

At Bavovna, we typically train custom hybrid INS navigation models on fused data from accelerometer, gyroscope, compass, barometer, and multi-vector airflow sensors to enable high-precision, autonomous flights in GPS-denied environments. But you can also layer LiDAR range data for extra corrections. 

What you gain as a result is:

• Drift reduction, thanks to continuous cross-correction of IMU errors by cross-referencing with stable sensor inputs
  
• Noise cancellation as filters suppress random spikes or jitter from individual sensors
  
• Flight even precision in GPS-denied environments, based on INS data alone
  
• Smoother control loops and reduced oscillation, thanks to improved PID 

• Greater situational awareness, along with real-time obstacle detection and  terrain-relative navigation

Machine Learning

Machine learning operationalizes fused sensor data into real-time intel for smoother navigation. Pre-trained models can recognize patterns of drift, turbulence, or interference before they destabilize the UAV and auto-adjust controls for smoother cruising. Bavovna’s AI Hybrid INS model, for instance, achieved 99.99% accuracy in GNSS-denied flight by continuously adapting to changing dynamics.

Also, you can train ML models for other use cases, like:

• Predictive maintenance. Algorithms can parse flight logs for early signs of motor imbalance, sensor degradation, or structural vibration, enabling proactive repairs.

• Energy optimization. Pre-trained models can learn from power-efficient flight patterns and dynamically manage throttle to extend battery life without sacrificing stability.

• Signal integrity monitoring. ML can detect jamming/spoofing signatures in RF/GNSS signals and suggest switching to other frequencies or inertial-based navigation. 

Mesh Networking

Mesh networking protocols allow drones to establish P2P communication links, instead of relying on a single ground control station or relay tower. 

Each UAV acts as both a node and a router, dynamically forwarding data packets between neighbors. This creates a Mobile Ad Hoc Network (MANET) where connections are self-forming and self-healing. If one link is lost due to terrain, interference, or node failure, data automatically reroutes through the remaining drones.  Flight controllers with integrated radios and networking protocols manage this routing in real time, ensuring continuous communication across the fleet and coordinated flying. 

This enables a host of new operational scenarios for: 

• ISR & tactical operations: Drone swarms can share live video and sensor feeds, even when only one has a direct uplink to the command center.
  
• Search & rescue missions: Mesh networking enables extended coverage across 

the operating site, relaying data to rescuers despite terrain obstacles.

• Disaster response: RC UAVs can provide a temporary communication infrastructure for first responders in affected sites by acting as aerial relays.
  
• Industrial inspections: Coordinated drone swarms can scan large industrial assets in less time and share data without overwhelming a single control link.

Conclusion 

Drone flight controllers have evolved from simple stabilizers to advanced gizmos for full autonomy. Thanks to edge computing, sensor fusion, ML, and mesh networking, you can fly your aircraft without second-guessing your commands or compromising flight stability. 

Learn how Bavovna’s AI hybrid INS kit enables greater autonomy in drone navigation by combining sensor fusion data with pre-trained AI models. 

Unmanned ground vehicles are evolving into fully capable robotic platforms. They integrate sensor arrays, edge computers, and AI models that read terrain, plan routes, and process decisions at machine speed. The focus is shifting from the mechanical frame to the technology stack that transforms raw inputs into precise navigation and mission-ready intelligence. In this post, we break down the core layers behind modern UGVs: perception sensors, navigation systems, and the AI engines that power autonomy.

This post looks into the ‘stack’ inside modern UGV that guides its movement and provides real-time intelligence for decision-making. 

1. Perception Sensors

At the center of every UGV lies a robust sensor array — its eyes and ears in the world. The two main categories are: 

• Exteroceptive sensors such as LiDAR, radar, ultrasonic sensors, and various types of cameras help sense the surroundings. They help detect obstacles, map terrain, and prevent collisions. 

• Proprioceptive sensors like inertial measurement units (IMUs), vehicle odometry, and GPS/GNSS are focused on tracking the UGV’s internal state: its speed, orientation, and position in space. 

The exteroceptive sensors often reinforce one another. For example, LiDAR can help deliver real-time 3D point maps of unknown terrain for greater spatial understanding. But it can be unreliable in poor weather conditions or near reflective surfaces (not to mention its price). Meanwhile, radars perform better in poor visibility conditions  (e.g., rain, fog), but offer lower spatial resolution than LiDAR. 

Some unmanned ground vehicles also rely on computer vision cameras and edge-deployed algorithms for perimeter scanning. While more compact models use ultrasonic sensors for
close-range obstacle detection. 

The key to peak performance? Sensor fusion. By meshing data from multiple sources — LiDAR for structure, radar for range, cameras for detail, and IMUs plus GNSS for location — UGVs form a reliable, accurate perception of their surroundings.  And this is something we do with our AI Navigation Kit. 

2. Navigation and Autonomy Components 

The next tech challenge is to make UGVs localize themselves and move purposefully, with varying levels of autonomy. 

Most vehicles rely on a GNSS (GPS) and INS (inertial navigation systems) combo for global and relative positioning that you need for mapping and waypoint tracking. Indoor UGS, in turn, may use laser beacon systems or marker-based navigation for pinpoint control. 

When it comes to autonomy, UGVs fall along a wide spectrum. At one end are fully teleoperated vehicles, where a human operator directs every movement. In the middle are semi-autonomous systems, which combine remote guidance with onboard assistance.
At the far end are fully autonomous vehicles, where AI independently makes navigation and task decisions.

For full autonomy, modern unmanned vehicles may rely on: 

• Real-time obstacle detection and adaptive path planning, powered by deep learning and reinforcement learning algorithms. 

• FMCW (Frequency Modulated Continuous Wave) radar for real-time terrain integrity analysis, which detects waterlogged or hidden voids.
  

Some systems, like Oshkosh’s TerraMax, combine LiDAR, multiple radars, cameras, and infrared sensors in a modular package for both autonomous and human-operated convoy control.

3. AI & Decision-Making

AI acts as the ‘brain’ for the bots that translates raw input data into adaptive, context-aware behavior.

Most UGV systems use pre-trained machine learning for object recognition, terrain analysis, and dynamic decision-making. This allows the vehicles to adapt to new scenarios on the fly and progressively improve their performance over time. 

New research is also pushing AI beyond navigation and into tactical autonomy. For example, one research group fused vision-language models with compact large language models to interpret complex battlefield scenes and craft multi-agent strategies, bridging perception and decision within a unified semantic space.

New AI systems are also being designed to monitor their own decision confidence. A recent framework used decision trees combined with predictive control to detect navigation errors or sensor faults and autonomously initiate recovery strategies, making UGVs safer and more reliable in uncertain environments. 

Lastly, more and more UGVs come with hybrid control models, where human operators and robots share decision-making. This approach lets robots handle routine or tactical decisions while humans intervene at strategic or safety-critical junctures — a balance between autonomy and oversight.

Looking Ahead

UGVs are no longer experimental prototypes. With sensor fusion, adaptive AI, and modular autonomy frameworks, they’re becoming trusted operators in defense, logistics, and industrial inspection. The pace of advancement makes one thing clear: the ground domain is entering an autonomy-first era

If you’re exploring how to equip your ground or aerial fleets with next-gen autonomy in GNSS-denied conditions, Bavovna’s AI navigation kit delivers proven field performance. Book a demo to see how our AI hybrid INS system can supercharge your mission. 

Most commercial drones come equipped with ultra HD, CMOS sensor-based cameras offering a crisp picture from the sky. But what if you need to fly at dawn, dusk, night, or through other environments where optics fail? That’s when UAV radar payloads step in. 

Radars turn drones into all-weather, all-terrain sensors, capable of spotting threats, mapping terrain, and holding altitude where other systems stumble. Below, we break down four popular UAV radar payload types (each with distinct strengths) for operators looking to give their platform a real mission edge.

Synthetic Aperture Radar (SAR) Payloads

SAR is a weather- or light conditions-agnostic alternative to using high-res cameras for UAV navigation. By analyzing echoes from bounced radar signals, it builds rich two- or three-dimensional images of landscapes, structures, and even hidden-from-view objects. For drones, that translates to extra ‘vision’ powers. 

Where optical sensors falter, SAR payloads keep watch over the UAV mission. For example, to assess damage or detect intruders at night, track camouflaged vehicles under foliage, or run UAV surveying missions through thick clouds or fog.  

The trade-off? SAR systems are larger, heavier, and need serious on-board processing power (aka not suitable for your average quadcopter). But if your mission calls for wide-area situational awareness in any conditions, SAR is a great choice for larger drones. 

Use cases:

• Coastal and border security 
• Disaster response and damage assessment
• Camouflage penetration and terrain change detection
• Wide-area military ISR in GPS-denied zones

Pulse-Doppler Radar Payloads

While SAR relies on static imagery, Pulse-Doppler radar leverages movement.  It measures tiny shifts in frequency to distinguish moving objects from a static background. For instance, a  Pulse-Doppler radar-equipped UAV can detect interceptor drones in contested airspace, spot speeding trucks, or zoom in on small boats against rolling seas. 

The big win here is clutter rejection: Pulse-Doppler radars are built to ignore background noise and lock onto moving threats. The downside? They can’t match SAR’s detail for static imaging. But if your priority is tracking what’s moving, Pulse-Doppler is the go-to.

Use cases

• Counter-drone detection and tracking
• Vehicle and aircraft pursuit in contested areas
• Anti-smuggling operations detection
• Real-time battlefield movement monitoring

Altimeter Radar Payloads

Altitude control is critical when you’re flying in dense urban areas, through rugged terrain, or in hostile territories. A few meters too low, and you risk losing your platform. Radar altimeters supply UAV operators with precise distance from the UAV to the ground, even when GPS is jammed or visibility nears zero.

Think of the cases where you need to lend near water, among heavy smoke, or amidst dense buildings. An onboard radar altimeter makes sure it stays at the right height — or touches down smoothly — no matter what the environment throws at it. 

Altimeters aren’t foolproof.  But as part of a broader payload mix (and with AI sensor fusion applied!), it can be a powerful add-on for safely cruising through difficult, GNSS-denied conditions.

Use cases

• Precision landing in zero-visibility conditions
• Terrain-following flight over rough or mountainous areas
• GNSS-denied or spoofed environment operations
• Flight safety for BVLOS missions
  

FMCW Radar Payloads

Frequency-modulated continuous-wave (FMCW) radar is the lightest and most compact UAV radar out of the pack. Thanks to digital signal processing, the FMCWs of today far exceed their predecessors in terms of resolution, efficiency, and versatility. Instead of sending radar signals in bursts, it continuously varies frequency to measure both distance and velocity. The result? Exceptional accuracy at close to mid-range.

This makes  FMCW a great UAV payload for collision avoidance and navigation in ‘busy’ environments. Think close-to-structure work like power line or wind turbine inspections. The technology can also aid with autonomous navigation in GPS-denied areas where obstacle detection is a must.

Yet, FMCW can’t match SAR or Pulse-Doppler for long-distance detection. But when space and weight are tight, and you need a reliable proximity sensor, it’s hard to beat.

Use cases:

• Obstacle detection and avoidance near infrastructure
• Urban or industrial inspection flights
• Safe operations around power lines, wind farms, or towers
• Autonomous UAV flights, together with other sensors

To Sum Up 

Each radar payload brings something different to the table. SAR delivers wide-area mapping in any conditions. Pulse-Doppler specializes in tracking what moves. Altimeter radars keep UAVs steady when GNSS falters. FMCW radars excel at short-range navigation and obstacle avoidance. 

The real edge, though, comes from pairing these payloads with a drone navigation system that doesn’t falter under pressure. That’s where Bavovna’s AI Hybrid Navigation Kit comes in. Purpose-built for GNSS-denied missions, it can fuse data from any number of sensors — IMU, radars, LiDAR, and more — to supply your UAV with accurate navigation data. If your goal is uncompromising performance in the toughest conditions, we know how to get you there.

Airflow sensors may not get much attention. But when it comes to staying airborne in unpredictable conditions, they’re one of the most important components on the drone. And you’d be glad to have that tech on board when the GPS signal goes down. 

Learn how airflow sensors work and where they make the biggest impact in keeping UAVs safe and steady.

What Is an Airflow Sensor? 

An airflow vector sensor tells your drone how the wind is behaving around it, e.g., how fast it’s blowing and from which direction. Mounted near the front of the airframe, it tracks airspeed, angle of attack, and sideslip, and passes the data on to keep the drone steady and responsive, regardless of the flight conditions. 

There are plenty of different types of airflow sensors on the market: 

• Static pilot tubes measure the difference between dynamic and static air pressure to calculate airspeed and airflow direction. 

• Hot-wire anemometers detect changes in airflow by measuring heat loss from heated wires exposed to wind. Other anemometers use omnidirectional hot ball probes for assessing airflow induced by drone rotors. 

• Ultrasonic anemometers time how fast sound waves travel between sensors to determine wind speed and direction. Because there are no moving parts to wear out, these have a good service life. 

• MEMS differential pressure sensors determine airspeed by comparing pressure from two different points on the drone’s body, usually via a tiny onboard chip. They’re lightweight, fast, and power-efficient, which makes them a smart fit for compact drones with limited battery lifespan.

In each case, the drone takes that airspeed data and blends it with inputs from its IMU to give you a cleaner picture of how it’s moving and what it would take to keep it on track. 

Why Drones Need Airflow Sensors

If you’re planning to fly beyond visual line of sight (or anywhere with unreliable GPS), an airflow sensor gives you a huge advantage. It supplies real-time data drones need to stay balanced, fly straight, and make it home, even when visibility drops or satellite signals cut out.

Thanks to airflow data, drone pilots can: 

• Stay stable in turbulent air. With real-time crosswinds, gusts, and airflow disruptions, you can correct orientation and hold course even through the wildest tempest. 

• Reduce stall and drift. Angle of attack and sideslip monitoring help avoid flight envelope violations during sharp maneuvers or slow-speed loitering, for example, during UAV mapping or border patrol missions. 

• Achieve smarter autonomy. Airflow data can be meshed with other INS readings and used to train AI flight algorithms for automated take-off, hovering, return-to-launch, and even full flight route execution.
  

Effectively, airflow provides real-time feedback on the environmental conditions, helping you maintain flight stability and accurate positioning where other sensors fall short.

Sample Use Cases for Airflow Sensors 

• Tactical ISR: In GPS-contested airspace, airflow data helps drones hover precisely and adjust for wind shifts that could compromise surveillance angles.
  
• Search & Rescue: Inside collapsed structures or narrow valleys, real-time airflow sensing keeps UAVs from drifting or crashing due to unexpected turbulence.
  
• Maritime and Desert Ops: With no visual landmarks and unreliable GNSS, airflow data combined with INS ensures orientation and controlled navigation over water, sand, or snow.

Airflow Sensors vs GPS Reliance: A Game Changer

Most drones rely heavily on GPS for navigation, but that’s not always a safe bet. GPS spoofers can be bought at online marketplaces, and signal jamming has become a common occurrence far away from the active conflict zones. Not to mention the general signal unavailability in remote zones or near high-EMI equipment. When the signal is down, a drone without a backup plan is just expensive hardware waiting to drift off course.

At Bavovna, we’ve been building drone navigation solutions to compensate for these shortcomings.  Our new 360-degree airflow sensor provides airspeed data with an accuracy of ±2% and wind vector measurement within ±5 degrees. When paired with data from an inertial navigation system (INS), they give the drone the ability to “feel” its way through the air, measuring how fast it’s flying, how it’s tilted, and whether it’s being pushed off course. 

And our complementary solutions, like our hybrid INS navigation kit and AirTower Mode, help you turn sensor readings into reliable navigation without any reliance on maps or GPS. 

UAVs are no longer just eyes in the sky. They’ve become sophisticated precision weapons and counter-weapons, too. 

As the technology has become more accessible, even starter drones can be reconfigured to carry explosives or act as self-destructing strike units with a bit of tinkering. Between 2019 and 2023, drone attacks by violent non-state actors — terrorist groups, insurgents, drug cartels — have more than doubled. The 2024 suspicious drone activity in New York City and across New Jersey has revealed just how vulnerable many urban environments can be to malicious UAVs. 

Traditional air defense solutions often fall short, as many UAVs are below their detection range. Plus, many react too slowly and cost too much to deploy at scale.  The rapidly emerging alternative is interceptor drones — a low-cost, high-precision, multi-purpose 

air defense platform.

What Is an Interceptor Drone? 

An interceptor drone is a purpose-built UAV for hostile drone detection, pursuit, and destruction.  Unlike regular tactical ISR drones, these platforms are designed for active engagement. Many models include advanced sensing and autonomous navigation capabilities for high-speed maneuvering, automatic target recognition, and precision hovering for threat neutralization. 

Key capabilities:

• High-speed pursuit
• Electronic warfare (EW) resistance
• Autonomous targeting
• Precision hovering
• Adaptive flight path correction
• GNSS-denied navigation
• Swarm coordination
• Multi-target engagement
  

Drone interceptors also come with a great range of payloads — net launchers, electronic jammers, or even kamikaze-style impactors. Many also rely on advanced navigation tech like AI-powered inertial navigation systems to safely cruise through contested, GNSS-denied zones.  

Examples of Inceptor Drones  

Ukraine has become a hotbed of interceptor drone innovation. To strengthen the country’s defences against Russian offenders, the local MilTech sector has been working on a host of new solutions. 

Ukrainian startup VARTA has recently unveiled the DroneHunter — a platform mounted with dual 12-gauge barrels and electronically triggered anti-drone rounds. It can neutralize aerial targets within a 5–20 meter range. Lightweight and easy to integrate, it requires only software calibration and basic hardware alignment.

Another Ukrainian-made solution, the Chief-1 Drone, is optimized for aerial dogfights. Its twin-barrel buckshot system, automatic target recognition, and manual or automatic fire modes make it effective against enemy FPV drones, bombers, and artillery spotters.

On the international front, Germany’s Alpine Eagle Sentinel system uses AI and edge computing to coordinate airborne sensors and interceptors. A single operator can control a swarm that detects, tracks, and disables rogue UAVs from a safe distance. The system is purpose-built for contested environments and loitering munitions.

From the U.S., DroneHunter F700 represents a different approach to drone interception. This AI-powered UAV uses net-based capture instead of destruction. Its onboard radar locks onto targets and autonomously adjusts for speed and direction. Afterwards, the UAV can intercept drones ranging from quadcopters to high-speed fixed-wing aircraft, with a human operator able to step in at any point.

How Interceptor Drones Enable Air-Tight Defense

Interceptor drones are changing the economics and agility of air defense. Unlike costly missile systems or slow-to-deploy ground-based radars, they offer real-time response at a fraction of the cost, and can adapt to new threats on the fly. 

Here’s what makes interceptor drones a game-changer for aerial defense: 

• Cost-efficiency. One interceptor drone can take down multiple threats without expending million-dollar munitions. And expendable “kamikadze” models are relatively cheap to produce — about $300 to $2,000, depending on the configurations. 

• Low-SWAP architecture. Compact and energy-efficient, long-wing interceptor drones can carry advanced payloads or run autonomous missions thanks to onboard edge processing units like Bavovna’s AI navigation kit. 

• Real-time responsiveness. The more advanced, AI-powered models can assess and distinguish between different types of threats mid-flight and adjust targeting strategies without much delay.
  
• Deployment flexibility. Many interception systems can be configured for deployment on FPV quadcopters or fixed-wing long-range drones. This enables a broader range of operational scenarios — from border patrols to base protection or event monitoring.  

Together, these capabilities enable fast, flexible, and scalable aerial defense — ideal for today’s asymmetric threat landscape.

The Next Stage — Autonomous Interception 

The future of air defense won’t be built on brute force, but on intelligent autonomy. As UAV threats become faster, smaller, and harder to predict, the next generation of interceptor drones must out-think as well as out-fly their targets. This shift is already underway. 

Bavovna’s AI Hybrid INS enables precise cruising and interception without relying on GPS. When powered by pre-trained algorithms, interception drones can operate in signal-jammed, low-visibility, or high-stress environments without a hitch. 

When autonomous navigation and hovering will be combined with the ability to detect, lock onto, and eliminate rogue drones without external commands, air defences will reach a new turning point. And with further advances in scalable architecture, drone interception systems will be adapted for swarms, mobile deployments, or even civilian event protection. 

Undeniably, exciting times are ahead for counter-drone technology as we move from stationary, often reactive, human-led detection and neutralization to autonomous, proactive anti-drone protections. 

From mining sites and crop fields to remote coastlines and disaster zones, UAV surveying is transforming how we capture and act on geospatial data. But speed and scale mean nothing without precision. When maps guide million-dollar positioning decisions, accuracy becomes mission-critical. 

In this article, we’ll break down the core technologies behind high-precision drone surveying, including how next-gen systems like AI-powered INS achieve sub-meter results, even in GNSS-denied environments where traditional tools flatline. 

RTK and PPK — the Backbone of UAV Surveying Accuracy

At the heart of modern drone land surveying lies high-precision GNSS (RTK/PPK). 

Built-in drone GNSS systems in most starter drones have an accuracy range of about 1-3 meters. More advanced commercial and military-grade models offer greater accuracy thanks to stronger receivers and real‑time kinematic (RTK) features.

RTK improves accuracy by continuously sending correction data from a fixed base station (or network) to the drone during flight. This stream of RTCM broadcasted corrections compensates for signal delays caused by atmospheric conditions, enabling the drone to calculate its position with greater precision. For example, DJI Matrice 4E, equipped with RTK, achieves about 1 cm + 1 ppm horizontal and 1.5 cm + 1 ppm vertical accuracy 

Meanwhile, post‑processing kinematics (PPK) performs equivalent corrections after the flight, using logged raw GNSS data, ensuring reliability even if the real‑time link drops mid‑mission. PPK enables even greater accuracy in UAV surveying missions. During field tests, WingtraOne PPK achieved 0.7 cm horizontal accuracy, with 1.3 cm horizontal / 2.3 cm vertical RMS across large survey areas. 

Many popular land surveying UAVs now include dual RTK + PPK systems that log raw GNSS data while flying in RTK mode, enabling post-flight PPK correction if needed, offering more consistent cm-level accuracy. 

But what happens when GNSS/GPS is down? 

Hybrid INS as an Alternative To RTK/PPK in UAV Aerial Surveying 

In high-interference zones or under GPS jamming/spoofing conditions, inertial navigation systems become a far better option for safe cruising. 

Unlike RTK or PPK, which hinge on a reliable GNSS signal, INS requires no external connectivity. By continuously integrating motion data from the UAV’s gyroscopes and accelerometers, the onboard unit uses dead reckoning to establish the UAV’s last known position and chart the subsequent flight path. 

The best internal navigation systems for UAVs include advanced capabilities like AI-powered sensor fusion, which combines data from gyroscopes, accelerometers, magnetometers, and barometric sensors to correct for drift in real time. 

Some systems, like Bavovna’s Hybrid AI-INS, go a notch further. Pre-trained on chaotic flight data and then up and running in real-time, our solution enables precision hovering, automated return-to-launch, and stable navigation even in GPS-jammed, spoofed, or zero-visibility environments.

Real-World Hybrid AI-INS Accuracy in Action

The true test of any INS is how it performs when conditions get tough. Bavovna’s Hybrid INS (H-INS) was recently deployed on an Aurelia X6 Max UAV in a mission designed to push its limits: a 30.88 km flight under GPS-denied conditions, in freezing temperatures (0°C), through a high-entropy flight pattern of chaotic maneuvers.

The UAV flew for 57 minutes, performing more than 30 km of unpredictable flight during a Loiter Phase before executing a fully autonomous Return-to-Launch (RTL) — all without GPS input or map reliance. Despite the complex dynamics, the system achieved an end-point accuracy of just 4.2 meters, with an overall positioning precision of 99.99%.

Why does this matter for UAV drone land surveying? In remote areas where GNSS is blocked, spoofed, or unreliable — dense forests, dusty pits, or urban canyons — hybrid AI-INS systems allow surveyors to maintain flight stability, accurate positioning, and mission continuity. Whether mapping large swaths of terrain or conducting critical infrastructure inspections, this level of precision ensures survey-grade data quality even when satellite-based solutions fail

Elevating Survey Standards with Next-Gen AI-INS Systems 

As drone surveying technology evolves, so do its capabilities to deliver precision in even the harshest environments. Traditional GNSS-based methods may falter under interference, but advanced solutions like AI-powered Hybrid INS are redefining what’s possible, providing centimeter-level accuracy where satellites can’t. 

For missions where positional drift simply isn’t an option—from UAV land surveying in remote terrains to critical infrastructure mapping—next-gen systems like Bavovna’s AI Navigation Kit offer unmatched reliability and precision. Explore UAV surveying tech that performs even where GPS can’t.

Modern UAVs have remarkable sensing abilities. Every pitch, yaw, and gust of wind is measured, interpreted, and corrected in real time—often without any signal from above.

At the heart of that awareness is the inertial measurement unit sensor. As UAV missions grow more complex, the quality of that IMU often marks the line between precision and failure. Here’s how to select the right one for your platform.

What Is an Inertial Measurement Unit Sensor?

An inertial measurement unit sensor (IMU) measures a UAV’s motion through space, tracking angular velocity, acceleration, and orientation using gyroscopes, accelerometers, and sometimes magnetometers.

In high-stakes conditions where GPS fails, an IMU becomes the UAV’s guiding force. Without it, a drone would drift, tilt, and spin its way into irrelevance, especially when GPS disappears and the drone cruises through featureless terrain. 

Key IMU Specs That Impact UAV Missions 

Not all inertial measurement unit sensors are made equal. Some drift slowly, like a boat losing its mooring. Others hold tight through turbulence, vibration, and electromagnetic interference. The difference lies in the details. When comparing IMUs, focus on:

• Drift rate. Indicates how far the IMU’s estimated position can veer from reality over time. In lower-tier units, drift builds quickly, turning flight into guesswork. Premium models, especially those fused with AI, keep deviation minimal.

• Bias stability and noise density affect how clean and consistent the sensor data is. High noise leads to jittery flight and poor hover control. Low noise keeps positioning steady. For missions in tight spaces or close to sensitive infrastructure, both matter more than raw speed.
  
• Update rate (Hz) reflects how frequently the IMU refreshes its data. Faster rates allow quicker reaction to wind shifts or sudden moves, crucial for indoor operations, urban missions, and unpredictable conditions.
  
• SWaP (Size, Weight, and Power). Every gram counts. A heavy or power-hungry IMU shortens flight time and limits payloads. Lightweight systems like  Bavovna’s 800-gram AI INS kit can deliver high-end performance without the drain.

Lastly, consider environmental resilience. The best IMUs are resistant to vibration, shielded against electromagnetic interference, and calibrated to withstand heat, cold, and pressure swings. Look for rugged models to avoid unsavory surprises midflight. 

How to Match the IMU to the Mission Profile

No internal measurement unit works in isolation. Effectiveness depends on how well its strengths align with the job.

UAV mapping missions demand surgical precision. A drone flying grid patterns over farmland or construction zones must maintain altitude and orientation to the centimeter. Even minor drift can corrupt data. Ultra-low noise, high refresh rates, and tight bias control are essential.

Surveillance and reconnaissance, especially in GPS-contested zones, require something different: steadiness without reliance on external signals. A drone loitering silently over a target must adapt to wind, terrain, and signal interference. Positioning must be consistent, even if GPS fails. This is where a hybrid INS, powered by machine learning and fused sensor data, keeps the aircraft focused and invisible — something we offer as part of our AirTower mode. 

Border patrols in remote zones bring their own challenges: empty landscapes, no signal towers, no landmarks. In these missions, an IMU with ultra-low noise density, high update rates, and tight bias stability becomes non-negotiable.

Across all scenarios, the IMU doesn’t just survive the flight — it defines its outcome. The sensor becomes the UAV’s inner compass. A poor choice risks the mission itself.

The Bottom Line

Choosing an IMU isn’t only about performance. Fit, efficiency, and reliability under pressure matter just as much.

That’s why Bavovna builds for real-world purposes, not just specs. Our AI-powered Hybrid INS sensors are trained on data from chaotic, signal-denied flights. Plug-and-play, cross-platform, and proven in the field. If your UAV has a mission worth completing, we’ll make sure it has the navigation to succeed. Learn more about our AI navigation kit.