Detect and Avoid Technology for BVLOS: The Complete 2026 Guide

Detect and Avoid (DAA) technology is becoming one of the most important safety systems in the commercial drone industry. As drones move beyond Visual Line of Sight (BVLOS), the remote pilot can no longer depend on human eyesight to identify approaching aircraft and maintain safe separation. Instead, the operation needs an alternative method of detecting traffic, assessing collision risk and enabling an appropriate avoidance response.

That makes DAA much more than a sensor mounted on a drone. A serious BVLOS detect-and-avoid architecture can combine airborne sensors, ground-based surveillance, electronic conspicuity, traffic information, U-space services, onboard processing, flight-control systems, remote pilots and operational procedures.

For a broader introduction to the subject, see our BVLOS Drone Operations in 2026: The Complete Guide to Beyond Visual Line of Sight.

The regulatory importance of DAA is also increasing. EASA's current 2026 framework explicitly treats BVLOS as requiring an alternative means of mitigation to human visual observation, with examples including DAA, TCAS, U-space and other forms of machine or machine-assisted mitigation.

At the same time, international standards are becoming more mature. ASTM International's F3442-25 defines performance requirements for DAA systems, while F3705-25 provides guidance for testing those systems. Together, these developments point toward a future in which BVLOS safety is increasingly demonstrated through measurable system performance rather than simply through claims about drone range or autonomy.

What Is Detect and Avoid?

Detect and Avoid, or DAA, is the combination of technology, software, procedures and human or automated decision-making used to help an unmanned aircraft detect potential traffic conflicts and take appropriate action to remain safely separated from other aircraft.

The concept is closely related to the traditional aviation principle of “see and avoid,” but it is designed for aircraft whose remote pilot cannot necessarily see the aircraft directly.

A simplified DAA chain looks like this:

• Detect: identify another aircraft or potential traffic threat.
• Track: estimate its position, direction, speed and movement.
• Assess: determine whether the aircraft represents a collision risk.
• Alert: provide information to the remote pilot or onboard system.
• Guide: determine an appropriate avoidance response.
• Avoid: command or support a maneuver that maintains the required separation.
• Recover: return the aircraft to its approved flight path when the conflict has been resolved.

The important point is that detection alone is not enough.

A camera that spots an aircraft is not automatically a complete DAA system. A radar that detects an object is not automatically a complete DAA system either.

The complete safety function extends from surveillance to decision-making and, where applicable, aircraft control.

Why DAA Is Essential for BVLOS

When a drone is flown in VLOS, the pilot or an appropriate observer can use human vision to identify other aircraft and react to potential conflicts.

BVLOS removes that direct visual capability.

That creates a fundamental aviation problem:

How does a remote pilot remain aware of traffic that the pilot cannot see?

The answer can involve several layers of technology and procedures.

EASA's current material recognizes that BVLOS tactical mitigation may involve ground-based DAA systems, airborne DAA systems, combinations of systems, human involvement or highly automated architectures.

This is particularly important as drones move from occasional flights toward persistent operations such as:

• power-line inspection
• railway monitoring
• pipeline inspection
• solar-farm inspection
• wind-turbine inspection
• construction surveying
• agricultural monitoring
• emergency response
• border and infrastructure monitoring
• maritime operations
• drone delivery
• drone-in-a-box operations

In these applications, BVLOS isn't simply about flying farther. It is about creating an operational system capable of safely functioning when the aircraft is outside direct human visual observation.

DAA and SORA 2.5

One of the most important developments for European BVLOS operators is the integration of DAA considerations into the risk-based SORA framework.

SORA 2.5 does not simply ask whether a particular drone has collision-avoidance technology.

Instead, the operation must be considered as a complete system, including the airspace environment, residual air risk and the performance of the tactical mitigation.

Within the SORA air-risk process, the initial Air Risk Class can be modified through strategic and tactical mitigations. Once strategic mitigations have been applied, remaining collision risk can require tactical mitigation.

For BVLOS operations, this can involve DAA or another accepted means of achieving the required airspace safety objective.

Importantly, the required level of tactical mitigation is linked to the residual air risk.

This distinction is crucial.

There is no universal “BVLOS DAA specification” that makes every operation acceptable.

The required architecture depends on the operational environment and the risk that remains after strategic mitigations have been applied.

Cooperative vs Non-Cooperative Traffic

One of the most important concepts in DAA engineering is the difference between cooperative and non-cooperative aircraft.

Cooperative Traffic

A cooperative aircraft provides electronic information that allows another system to identify or track it.

Depending on the aviation environment, this can include systems such as:

• ADS-B
• ADS-L
• FLARM
• SSR-derived surveillance
• TCAS-related information
• other electronic conspicuity technologies

Cooperative surveillance can provide valuable information such as position, altitude, direction and speed.

However, cooperative surveillance has an important limitation:

An aircraft that does not transmit usable information may not appear in the electronic traffic picture.

Non-Cooperative Traffic

Non-cooperative traffic does not depend on the target aircraft transmitting information.

Instead, the DAA system attempts to detect the aircraft physically or through external surveillance sources.

Possible technologies include:

• radar
• electro-optical cameras
• infrared cameras
• LiDAR
• passive RF sensing
• acoustic sensing
• ground-based surveillance
• networked sensor systems

Non-cooperative detection is especially important because general aviation aircraft, helicopters, gliders, microlights and other low-altitude users may not always provide the electronic information that a drone expects.

This is why a robust DAA architecture increasingly needs to think in terms of cooperative plus non-cooperative surveillance rather than choosing only one.

Radar for Drone Detect and Avoid

Radar is one of the most technically important DAA technologies because it can detect targets without relying on the target aircraft to transmit information.

A radar-based system can potentially determine:

• range
• bearing
• relative velocity
• track history
• approach trajectory

Modern compact radar technology is particularly interesting for drones because advances in solid-state electronics and signal processing have made sophisticated sensing possible in relatively small packages.

Advantages of Radar

• Works independently of target cooperation.
• Can operate during day and night.
• Can function in conditions where optical cameras are degraded.
• Provides direct range information.
• Can support continuous tracking.

Limitations of Radar

• Small aircraft can have limited radar signatures.
• Vegetation and structures can create clutter.
• Low-altitude environments are technically challenging.
• Detection performance depends strongly on antenna design and environment.
• Radar alone does not automatically determine whether an object is a collision threat.

The final point is particularly important.

Detection is only the first step.

A DAA system must interpret the detected object's movement and determine whether a conflict exists.

Electro-Optical and Infrared DAA

Cameras can provide another layer of airspace awareness.

Electro-optical systems can potentially identify aircraft visually, while infrared sensors can provide additional capability in low-light or nighttime conditions.

Computer vision is becoming increasingly important because modern processors can analyze camera feeds in real time.

A computer-vision DAA system may attempt to:

• detect aircraft-like objects
• estimate their position in the image
• track their movement
• estimate relative motion
• classify potential threats
• combine visual observations with other sensors

However, optical DAA has obvious limitations.

Aircraft can be extremely small at long distances. Atmospheric haze can reduce contrast. Lighting can change rapidly. Clouds, glare and background clutter can make detection difficult.

This is why professional DAA architectures are increasingly focused on sensor fusion rather than relying exclusively on a single camera.

LiDAR and DAA

LiDAR can contribute highly accurate three-dimensional environmental information.

For BVLOS systems, LiDAR is generally more relevant to environmental perception and obstacle detection than to long-range detection of conventional aircraft, although specialized architectures can explore broader applications.

Its strongest value may therefore be complementary:

Radar detects distant traffic while LiDAR and vision improve local environmental awareness.

This becomes particularly important during avoidance maneuvers.

A drone may detect an approaching aircraft and determine that it must maneuver. But the selected maneuver must also remain compatible with terrain, buildings, towers, power lines and the operational volume.

DAA therefore cannot be treated as an isolated air-traffic sensor.

ADS-B, ADS-L and Electronic Conspicuity

Electronic conspicuity is becoming an increasingly important component of European airspace integration.

ADS-B allows appropriately equipped aircraft to broadcast information that can support traffic awareness.

However, the European ecosystem is also developing lighter-weight solutions.

ADS-L, or Automatic Dependent Surveillance-Light, is designed as a lower-cost, lower-power approach to electronic conspicuity. EASA's ADS-L 4 SRD860 specification is intended to help aircraft become electronically conspicuous, particularly in the context of U-space and shared airspace.

In December 2025, EASA published Issue 2 of the ADS-L 4 SRD860 specification, adding improvements including ground-to-air retransmission capabilities and interoperability-related enhancements.

In May 2026, EASA and EUROCONTROL also released electronic-conspicuity use cases intended to support safer shared airspace and better integration between manned and unmanned aviation.

This is important for BVLOS because the future traffic picture may not come from one sensor.

Instead, it may be assembled from:

• airborne radar
• ADS-B
• ADS-L
• ground-based sensors
• U-space services
• networked surveillance
• other electronic-conspicuity systems

The result could be a much richer picture of the surrounding airspace.

Is Remote ID the Same as DAA?

No.

Remote ID and DAA solve different problems.

Remote identification is primarily concerned with identifying or broadcasting information about a UAS and its operation.

DAA is concerned with detecting traffic conflicts and helping prevent collisions.

Remote ID can therefore contribute to situational awareness in some architectures, but it should not automatically be treated as a complete collision-avoidance system.

This distinction is critical when evaluating drone specifications.

Why Sensor Fusion Matters

The future of DAA is unlikely to be defined by one perfect sensor.

Instead, the strongest architectures are likely to combine multiple information sources.

Consider a hypothetical BVLOS inspection drone.

Its system could receive:

• radar detection of an aircraft at long range
• ADS-B information from cooperative traffic
• U-space traffic information
• camera-based confirmation
• GNSS position of the drone itself
• flight-plan information
• terrain and obstacle data

A sensor-fusion engine can combine these inputs into a single traffic model.

The system can then estimate:

• relative position
• relative velocity
• closest point of approach
• time to closest approach
• uncertainty
• potential conflict probability

This is far more powerful than simply displaying several independent sensor feeds to a remote pilot.

From Detection to Collision Prediction

A DAA system ultimately needs to answer one of the most important questions in aviation:

Is this traffic actually a threat?

Not every detected aircraft creates a collision risk.

An aircraft several kilometers away traveling in the opposite direction may never come close to the drone.

Another aircraft may initially appear distant but be approaching rapidly.

The DAA system therefore needs to consider the geometry and dynamics of the encounter.

Important parameters can include:

• relative position
• relative speed
• heading
• altitude
• vertical rate
• tracking uncertainty
• time to closest approach
• predicted separation
• aircraft performance
• available maneuvering space

This is where DAA starts to become a sophisticated aviation safety system rather than a simple object-detection application.

What Is “Well Clear”?

One of the central concepts in collision avoidance is maintaining sufficient separation from another aircraft.

The exact criteria depend on the applicable regulatory and technical framework, but a DAA system generally needs a defined concept of what constitutes an unacceptable encounter.

The system may therefore have several levels of alert:

The objective is not necessarily to wait until two aircraft are close to each other.

A good DAA system needs enough time to detect, analyze and respond before the encounter becomes critical.

Human-in-the-Loop vs Autonomous DAA

DAA does not automatically mean that the drone flies completely autonomously.

There are several possible architectures.

Pilot-Assisted DAA

The system detects traffic and presents information to the remote pilot.

The pilot makes the final decision and controls the aircraft.

Decision-Support DAA

The system detects traffic, calculates the conflict and proposes an avoidance maneuver.

The remote pilot evaluates and executes the recommendation.

Automated Avoidance

The system can execute predefined avoidance logic automatically under appropriate conditions.

The remote pilot remains responsible for supervision and intervention where required by the approved operation.

Highly Autonomous DAA

The aircraft continuously monitors traffic and environmental information and can perform avoidance decisions with minimal direct pilot input.

This architecture is technically attractive for large-scale autonomous operations, but the safety case, system assurance and regulatory framework become correspondingly more demanding.

DAA and U-Space

DAA and U-space are related but they are not the same thing.

DAA is a conflict-management capability.

U-space is a broader digital airspace ecosystem.

U-space can provide services such as:

• flight authorization
• network identification
• geo-awareness
• traffic information
• common information exchange

Traffic information can give operators awareness of known traffic in proximity to their operations.

U-space can therefore become an important information layer within a wider BVLOS safety architecture.

But U-space should not automatically be interpreted as a replacement for every onboard DAA function.

A professional operation may combine:

U-space + electronic conspicuity + airborne sensing + ground surveillance + remote pilot procedures.

Ground-Based DAA vs Airborne DAA

There are two broad ways to build DAA coverage.

Airborne DAA

The sensors travel with the drone.

Advantages include:

• coverage moves with the aircraft
• direct access to onboard flight-control systems
• potentially independent operation away from ground infrastructure
• useful for mobile missions

Disadvantages include:

• payload and power requirements
• limited sensor horizon
• aircraft integration complexity
• weight constraints

Ground-Based DAA

Ground sensors monitor the surrounding airspace and provide information to the drone or remote operations center.

Advantages include:

• larger sensors can be used
• multiple aircraft can share surveillance infrastructure
• persistent monitoring is possible
• high-performance radar can remain permanently installed

Disadvantages include:

• coverage depends on sensor location
• terrain can create blind areas
• infrastructure costs can be significant
• communications become important

For complex operations, the strongest architecture may combine both.

DAA Is Also About the Avoidance Maneuver

Detecting another aircraft is only useful if the system can respond safely.

An avoidance maneuver must consider much more than the intruder.

The aircraft may also need to account for:

• terrain
• buildings
• power lines
• bridges
• restricted airspace
• operational-volume boundaries
• weather
• remaining battery
• navigation accuracy
• C2-link status

Imagine a drone flying along a power line.

A DAA system detects a helicopter approaching from the side.

The theoretically simplest response might be to move sideways.

But moving sideways could place the drone directly into another hazard.

A mature system therefore needs to find an avoidance maneuver that resolves the air conflict while remaining safe in the surrounding environment and consistent with the operational constraints.

DAA and the Command-and-Control Link

DAA performance is closely connected to the aircraft's command, control and communications architecture.

A detection system can be excellent, but the operation can still be unsafe if critical information cannot reach the remote pilot or if an avoidance command cannot reliably reach the aircraft.

A BVLOS architecture may therefore need to consider:

• C2 availability
• latency
• link continuity
• coverage
• redundancy
• lost-link behavior
• cybersecurity
• traffic-data latency

This is one reason technologies such as 4G, 5G and other communications architectures are becoming increasingly relevant to autonomous BVLOS systems.

For more on this part of the ecosystem, see our 5G Drones: How Cellular Networks Could Make Autonomous BVLOS Flight Possible.

ASTM F3442-25: A Major DAA Standard

One of the most important developments in DAA standardization is ASTM F3442-25, Standard Specification for Detect and Avoid System Performance Requirements.

The standard is designed around smaller unmanned aircraft encountering crewed aircraft in lower-risk airspace environments.

Importantly, F3442-25 is architecture agnostic.

That means it does not simply mandate one sensor technology.

Instead, it establishes performance expectations that a DAA system needs to meet.

This is an important direction for the industry.

The question becomes less:

“Does the drone use radar?”

and more:

“Can the complete DAA system demonstrate the required safety performance?”

The standard also addresses issues such as detection range, the time available to achieve well-clear conditions and safety-related encounter metrics.

ASTM F3705-25: Testing DAA Systems

A safety architecture is only as credible as its verification.

ASTM F3705-25 provides a guide for testing DAA systems and describes approaches for demonstrating performance against the requirements of the DAA specification.

The testing concept covers important elements such as:

• surveillance
• alerting
• guidance
• operator response
• software and hardware testing
• simulation
• integrated testing
• real-world testing

This is particularly significant because DAA is not just a hardware problem.

The sensor, software, algorithms, displays, pilot procedures and aircraft flight controls all interact.

A radar might perform correctly in isolation while the complete system fails to produce an appropriate alert quickly enough.

Testing therefore needs to examine the complete chain.

Why AI Could Transform DAA

Artificial intelligence and machine learning could significantly expand DAA capabilities, particularly for non-cooperative detection.

AI can potentially help classify objects, reduce false alarms and interpret complex sensor data.

For example, a vision system might detect several moving objects:

• bird
• aircraft
• helicopter
• construction crane
• vehicle
• weather phenomenon

The system needs to distinguish meaningful aviation threats from irrelevant detections.

AI-based perception could help.

However, AI should not be treated as a magic solution.

Safety-critical autonomous systems require evidence that the perception and decision-making functions behave reliably within their intended operational environment.

That makes validation, verification, uncertainty management and system assurance just as important as raw AI performance.

DAA Challenges in Real-World Operations

Laboratory performance can look impressive.

The real world is harder.

Small Targets

A small aircraft can occupy only a few pixels in a camera image at long range.

Low-Level Traffic

Helicopters and light aircraft can operate at low altitudes where terrain and buildings create difficult radar and optical conditions.

Birds

Birds can create false detections and can themselves represent hazards, even though certain DAA standards may not cover bird avoidance.

Weather

Rain, fog, snow, haze and strong sunlight can degrade sensor performance.

Terrain and Clutter

Mountains, trees, buildings and other structures can obstruct detection.

Latency

A traffic position that is several seconds old may not be adequate for a fast encounter.

Sensor Failure

Professional systems need to consider what happens when a sensor becomes unavailable or unreliable.

Communications Failure

The system needs defined behavior if traffic information or C2 connectivity is interrupted.

These challenges explain why serious BVLOS operations require an integrated safety case rather than simply installing a “collision avoidance sensor.”

DAA for Drone-in-a-Box Operations

Drone-in-a-box systems are one of the strongest commercial applications for advanced DAA.

A permanently deployed aircraft can potentially perform repeated inspection missions without sending a pilot to the site for every flight.

But this also means the aircraft may repeatedly enter the same airspace without a human standing next to it.

DAA becomes central to the architecture.

A mature drone-in-a-box system could combine:

• automated launch
• precision navigation
• airborne DAA
• ground surveillance
• U-space information
• remote operations
• automated return
• weather monitoring
• automated landing
• continuous system health monitoring

For a deeper look at this operating model, read our Drone-in-a-Box 2026: The Complete Guide to Autonomous Drone Stations.

What a Professional BVLOS DAA Architecture Looks Like

A sophisticated system can be visualized as several interconnected layers.

This layered approach is much closer to how the industry should think about DAA in 2026.

How to Evaluate a DAA System

Operators evaluating BVLOS technology should avoid choosing systems based solely on marketing claims.

Instead, ask the following questions.

1. What Traffic Can It Detect?

Does it detect cooperative traffic, non-cooperative traffic or both?

2. What Is the Proven Detection Performance?

Look for measurable performance data rather than generic statements such as “advanced obstacle avoidance.”

3. What Are the Environmental Limitations?

Understand performance during darkness, fog, rain, glare, vegetation and low-altitude operations.

4. What Happens When a Sensor Fails?

A serious safety architecture should have defined degraded modes.

5. How Is Traffic Information Processed?

Is the system simply displaying traffic, or does it perform automated threat assessment?

6. Who Makes the Avoidance Decision?

The remote pilot, onboard software or a combination?

7. How Does It Interface With Flight Control?

DAA information needs to reach the aircraft's flight-control architecture appropriately.

8. How Has It Been Tested?

Ask whether testing includes simulation, hardware-in-the-loop, flight testing and integrated system testing.

9. What Standards Apply?

Determine whether the system has been developed or assessed against relevant aviation standards and means of compliance.

10. Can the DAA Architecture Support the Intended SORA?

This may be the most important question for a European operator.

The objective is not to buy the most sophisticated sensor.

The objective is to build a complete operational safety case that satisfies the requirements of the intended operation.

DAA Is Not the Same as Obstacle Avoidance

This distinction is often misunderstood.

Obstacle avoidance is primarily concerned with objects such as:

• buildings
• trees
• towers
• power lines
• terrain

Detect and Avoid for air traffic is primarily concerned with aircraft conflicts.

The two systems can share sensors and computing resources, but their objectives are different.

A drone can have excellent obstacle avoidance and still lack the capability required to detect a small helicopter approaching from several hundred meters away.

This is another reason consumers and operators should be careful when a drone manufacturer describes a product as having “AI obstacle avoidance” and assumes that this means the aircraft has a complete BVLOS DAA capability.

DAA and the Future of Autonomous Drones

The importance of DAA will increase as drones become more autonomous.

A conventional drone mission might involve:

Pilot → Drone → Camera → Return

A future autonomous commercial operation could look more like:

Mission software → Aircraft → Sensor fusion → DAA → AI decision-making → U-space → Remote operations center → Fleet management

The aircraft becomes one part of a much larger aviation system.

This is particularly relevant to:

• automated infrastructure inspection
• drone delivery
• energy-sector monitoring
• emergency response
• large-scale surveying
• autonomous security patrols
• remote industrial operations
• multi-drone fleets

As fleet sizes increase, the economics of manually managing every aircraft become increasingly difficult.

That creates pressure for automation.

But automation only becomes scalable when the safety architecture scales with it.

The Future: Multi-Sensor, Networked DAA

The most promising future architecture may not be a single DAA sensor at all.

It could be a network.

Imagine a solar farm with several autonomous drones.

The system receives information from:

• drone-mounted radar
• EO/IR sensors
• electronic-conspicuity systems
• ground radar
• U-space services
• weather sensors
• other drones
• airspace information services

All of this information can feed a common operational picture.

Instead of each aircraft operating as an isolated machine, the fleet becomes part of a connected airspace-management ecosystem.

This could eventually support increasingly sophisticated concepts such as:

• multi-drone operations
• dynamic route changes
• automated conflict resolution
• shared surveillance
• remote operations centers
• persistent aerial monitoring
• automated emergency responses

That is where DAA becomes much more than a drone feature.

It becomes one of the enabling technologies for an autonomous aviation ecosystem.

What DAA Means for Spain and Europe in 2026

For European operators, DAA should be considered in the context of the EU UAS regulatory framework and the applicable national authority requirements.

In Spain, AESA is the national competent authority for civil aviation safety matters relevant to UAS operations.

For operations in the specific category, the operator needs to consider the applicable regulatory pathway, operational environment, geographical zones, risk assessment and mitigations.

For BVLOS missions, DAA can become particularly important where the operation requires tactical mitigation of residual air risk.

Operators should therefore avoid approaching DAA as a product-shopping exercise.

The correct sequence is:

1. Define the intended operation.
2. Understand the airspace environment.
3. Determine the initial and residual air risk.
4. Identify the applicable tactical mitigation requirements.
5. Define the DAA performance needed.
6. Select the appropriate architecture.
7. Integrate it with the aircraft and C2 system.
8. Verify and validate the complete system.
9. Document the safety case.
10. Submit the applicable evidence to the competent authority.

DAA Technology Checklist for BVLOS Operators

MidronePro Verdict

Detect and Avoid is one of the most important technologies in the transition from remotely piloted drones to scalable autonomous aviation.

BVLOS cannot be reduced to transmission range.

It cannot be reduced to autonomy.

And it cannot be reduced to obstacle avoidance.

A professional BVLOS operation requires an integrated architecture capable of managing the risks created when the remote pilot cannot rely on direct visual observation.

That architecture can include:

• radar
• EO/IR perception
• LiDAR and environmental sensing
• ADS-B
• ADS-L
• FLARM
• ground-based surveillance
• U-space services
• sensor fusion
• AI-based perception
• traffic prediction
• remote-pilot alerting
• automated avoidance
• resilient C2 communications
• flight-control integration

The regulatory environment is also becoming more technically mature. EASA's June 2026 Easy Access Rules incorporate SORA 2.5 and explicitly recognize DAA and other machine-assisted means as potential BVLOS tactical mitigations. Meanwhile, European electronic-conspicuity initiatives such as ADS-L are developing alongside international DAA standards.

ASTM F3442-25 and F3705-25 are particularly significant because they move the conversation toward measurable performance and structured testing.

That is exactly the direction the industry needs.

The future of BVLOS will not be determined by the drone with the longest advertised range.

It will be determined by the systems that can demonstrate:

detect → understand → decide → avoid → recover.

And as autonomous drones become persistent infrastructure rather than occasional aircraft, DAA may become one of the defining technologies separating experimental BVLOS operations from genuinely scalable commercial aviation.

Learn More at MidronePro Academy

Want to explore the wider technology and regulatory ecosystem behind BVLOS operations?

• BVLOS Drone Operations in 2026: The Complete Guide — Understand the complete architecture behind beyond-visual-line-of-sight operations.
• Autonomous Drones in 2026: AI, BVLOS, Drone-in-a-Box, Swarms & the Future of Aerial Robotics — Explore the technologies turning drones into autonomous aerial systems.
• Drone Laws in Spain 2026: Complete Guide to Flying Legally and Safely — Review the Spanish regulatory environment before planning professional drone operations.
• Where Can I Fly My Drone? Best Drone Airspace Maps & AirMap Alternatives in 2026 — Learn how airspace information and geographical restrictions affect drone operations.

Frequently Asked Questions About Detect and Avoid Technology for BVLOS

What is Detect and Avoid (DAA)?

Detect and Avoid is a combination of sensors, surveillance systems, software, procedures and decision-making capabilities used to help an unmanned aircraft detect potential traffic conflicts and maintain safe separation from other aircraft during operations such as BVLOS.

Why is DAA important for BVLOS drones?

During BVLOS operations, the remote pilot cannot rely on direct visual observation of the aircraft and surrounding traffic. DAA provides an alternative means of detecting, assessing and responding to potential airborne conflicts.

What sensors can be used for drone DAA?

DAA architectures can use radar, electro-optical and infrared cameras, electronic-conspicuity systems, ADS-B, ADS-L, FLARM, ground-based surveillance, LiDAR, RF sensing and other technologies. The appropriate combination depends on the operation and required safety performance.

Is obstacle avoidance the same as Detect and Avoid?

No. Obstacle avoidance focuses primarily on terrain and objects such as buildings, trees, towers and power lines. DAA for air traffic focuses on detecting and avoiding other aircraft. Some systems can share sensors and processing resources, but the safety functions are different.

Is Remote ID a Detect and Avoid system?

No. Remote ID primarily provides identification and information about a UAS. It can contribute to situational awareness in some architectures, but it is not automatically a complete traffic-detection and collision-avoidance system.

Can radar detect non-cooperative aircraft?

Radar can provide non-cooperative detection because it does not require the target aircraft to transmit identification information. However, performance depends on factors such as aircraft size, radar design, range, altitude, terrain, clutter and environmental conditions.

Does a drone with DAA automatically have permission to fly BVLOS?

No. DAA technology does not itself provide regulatory authorization. The complete operation must satisfy the applicable aviation rules, risk assessment, tactical mitigation requirements, operational procedures and competent-authority approval or declaration pathway.

How does DAA relate to SORA 2.5?

Within the SORA framework, tactical mitigations can be used to address residual air risk. For BVLOS operations, DAA or another accepted alternative can provide the means of mitigating the remaining risk. The required robustness depends on the residual air-risk category and applicable tactical mitigation performance requirements.

What is the difference between cooperative and non-cooperative DAA?

Cooperative DAA uses information transmitted or made available by another aircraft or surveillance system, such as ADS-B, ADS-L or FLARM. Non-cooperative DAA attempts to detect aircraft without relying on the target to transmit information, using technologies such as radar or optical sensing.

Will AI make BVLOS DAA fully autonomous?

AI can improve object detection, classification, tracking and sensor fusion, but autonomous DAA also requires reliable decision-making, aircraft control, verification, validation, operational procedures and appropriate regulatory assurance. AI is an enabling technology, not a substitute for the complete safety architecture.