Lessons from Ukraine: What the Battlefield Teaches Us About C-UAS

Executive Summary

The war in Ukraine has become an unplanned, large-scale operational test of counter-drone technology and tactics. Over three years of conflict, Ukrainian and Russian forces have fielded thousands of unmanned aircraft across all domains of combat: surveillance, strike, swarm attack, and strategic targeting. The scale, complexity, and rapid innovation pace observed in Ukraine have directly informed U.S. military C-UAS strategy and are reshaping vendor roadmaps and procurement requirements.

However, Ukrainian operational lessons do not directly translate to U.S. military doctrine or critical infrastructure protection. Ukraine operates in an exceptionally permissive electronic warfare environment, with neither side constrained by spectrum regulations or international law on frequency use. Threat profiles are dominated by low-cost, expendable drones—a distinct threat class from adversary systems facing U.S. military and homeland defense.

This briefing extracts the most relevant operational lessons from Ukraine, explores their applicability to U.S. environments, and addresses implications for C-UAS procurement and capability development.

Part 1: Electronic Warfare Dominates the C-UAS Battlespace

EW as the Primary Effector

Ukraine has fought a fundamentally electronic warfare-driven counter-drone campaign. The vast majority of drone attrition—estimates range from 60-75%—comes from RF jamming, GPS spoofing, and signal degradation rather than kinetic intercept. Ukrainian forces deploy jamming systems ranging from vehicle-mounted RF emitters to handheld devices capable of disrupting drone control signals and navigation systems.

Russian forces employ similar tactics, using deployed RF jammer networks to defend against Ukrainian attack drones. Both sides have adapted jamming techniques to adversary countermeasures, creating a continuous cycle of electronic countermeasure and counter-countermeasure innovation.

Why EW Dominates in Ukraine

Three factors explain EW's dominance:

1. Cost Asymmetry Favors EW: Kinetic intercept requires expensive air defense systems (missile-based, cannon-based, or directed energy). A single Surface-to-Air Missile (SAM) costs tens of thousands of dollars and requires pre-positioned platforms and trained operators. A jamming emitter costs tens of thousands of dollars for the equipment but can degrade unlimited numbers of drones for the duration of operation. EW provides superior cost-per-engagement.

2. No Spectrum Regulation: Neither Ukrainian nor Russian forces operate under spectrum regulations. Both sides freely transmit jamming signals on any frequencies supporting drone communications. In a regulated environment (like the United States), this approach would violate the Communications Act. In Ukraine, it is standard practice.

3. Drone Designs Lack Robust Anti-Jam Architecture: Most Ukrainian and Russian drones in service were designed for low-threat environments without expectation of sophisticated jamming. They lack frequency-hopping, encrypted control channels, or automated failover to alternative navigation modes. Jamming is therefore highly effective against legacy designs.

EW Limitations Observed in Ukraine

EW also has critical limitations that Ukraine's experience reveals:

Signal Injection and Spoofing Complexity: Effective GPS spoofing requires understanding the specific receiver architecture and signal processing. Generic jamming (broadband noise injection) is easier than spoofing (transmitting false but coherent GPS signals). Ukrainian operators often rely on broadband jamming rather than precision spoofing, which is less effective against receivers with anti-spoofing filters.

Reciprocal Vulnerability: Jamming emitters transmit continuously, creating observable RF signatures. Adversary drones equipped with RF direction-finding receivers can locate jamming sources and either avoid them or target them with attack drones. Ukrainian and Russian forces both engage adversary jamming emitters when detected.

Frequency Adaptation Cycles: When one side implements jamming on specific frequencies, the other adapts. Ukraine observed Russian drones switching control frequencies within days of large-scale jamming deployment. This rapid adaptation cycles—measured in days or weeks—outpace equipment procurement and force structure changes (measured in months or years).

Applicability to U.S. Environments

Limited Direct Applicability: Ukraine's EW-dominated C-UAS approach is not directly applicable to U.S. military or homeland defense because:

• Spectrum Regulations: The U.S. and allied nations operate under FCC and international spectrum agreements. Deploying jamming systems faces legal constraints and coordination requirements that Ukraine does not.
• Drone Design Quality: U.S. military and adversary drones have robust anti-jam architecture, frequency hopping, and encrypted control channels. Generic EW is significantly less effective against these designs.
• Allied Coordination: U.S. military operations over allied territory require airspace coordination and spectrum sharing. Large-scale jamming deployment must account for impacts on allied systems and civilian communications.

Indirect Lesson: EW will remain a critical C-UAS component in future conflicts, particularly in contested electromagnetic environments where adversary jamming is present. U.S. military C-UAS architecture must incorporate EW resilience (anti-jam design, frequency agility, encrypted control) as a foundational requirement.

Part 2: Kinetic Intercept Struggles with Threat Volume

Kinetic Systems Achieve Limited Destruction Rates

Ukraine has deployed various kinetic C-UAS systems: cannon-based air defense (Vulcan, Gepard), missile systems (Patriot, Tor, Avenger), and directed energy concepts. Despite significant investment in kinetic systems, destruction rates remain below 50% in high-threat scenarios. When Ukrainian forces face sustained drone swarms (10+ simultaneous aircraft), kinetic engagement rates drop precipitously.

Russian forces experience similar limitations. The Pantsir system (an integrated radar and cannon air defense platform) achieves higher destruction rates than unguided air defense but still falls short of 70% effectiveness in documented engagements against coordinated drone attacks.

Why Volume Defeats Kinetic Systems

Engagement Rate Constraints: A cannon-based air defense system can deliver approximately 6-10 rounds per minute. Each round has a 30-50% probability of hit against a maneuvering drone. Engaging multiple simultaneous targets at different ranges and vectors requires engaging aircraft sequentially, not simultaneously. A swarm of 20 drones can overwhelm a single cannon platform in seconds.

Missile-based systems have better hit probability but much lower rate-of-fire. A Patriot battery can engage 1-2 targets simultaneously and requires 30-60 seconds between engagements. A coordinated swarm attack easily exceeds system engagement capacity.

Cost Asymmetry: A single air defense missile costs $150,000 to $500,000, depending on system (Patriot, Avenger, Tor). A drone attrited costs $500 to $50,000, depending on type. One missed engagement or engagement of a low-value target rapidly depletes munition stocks. Ukraine has experienced extended periods where air defense systems were constrained by munition availability despite continued drone threats.

Target Complexity: Distinguishing high-value drones (carrying ISR sensors or large strike payloads) from expendable FPV attack drones or reconnaissance quadcopters requires rapid classification. Engaging every detected drone is economically unsustainable. Selecting high-value targets requires intelligence and prediction, which fails under rapid attack sequences.

Adaptation to Kinetic Constraints

Ukraine and Russia have both adapted operational tactics to kinetic intercept limitations:

Distributed Deployment: Instead of concentrating air defense at high-value targets, both sides distribute air defense coverage across broader areas. This increases total defense capability but reduces layered coverage at any single point.

Unmanned Counter-Air: Both sides deploy their own drones to engage adversary drones, effectively using low-cost drones to destroy adversary drone operations. This approach distributes costs (multiple cheap drones for each engagement) but is tactically effective.

Deception and Decoy Tactics: Attackers deploy expendable decoys (inert drones providing signatures without threat capability) alongside actual threat drones, saturating and consuming kinetic intercept capacity.

Applicability to U.S. Environments

Swarm Defense as Requirement: The kinetic engagement challenges observed in Ukraine apply directly to U.S. military threat planning. Adversary drone swarms (whether Russian, Iranian, Chinese, or North Korean designs) will present volume-based engagement challenges. U.S. military C-UAS procurement must prioritize:

• Rapid engagement cycles: Systems capable of engaging multiple targets per minute, not per minute-scale cycles.
• Probability of hit optimization: High-confidence intercept against maneuvering drones with small radar cross-sections.
• Layered defense: Multiple C-UAS modalities (EW, optical, kinetic) working in coordination, so kinetic systems engage only high-confidence high-value threats.

Cost Sustainability: The cost-per-engagement asymmetry observed in Ukraine is directly relevant to U.S. procurement strategy. If air defense munitions cost $200,000 per engagement and drones cost $5,000 to $50,000 per aircraft, sustained drone swarm defense will rapidly deplete munition stocks. Procurement strategy must either:

1. Develop low-cost kinetic intercept (using cheaper munitions or non-kinetic engagement)
2. Increase munition production to sustain continuous engagement
3. Accept incomplete defense and prioritize high-value targets over comprehensive coverage

Current U.S. military procurement strategy does not adequately address cost asymmetry. Relying on existing air defense systems (designed for manned aircraft) to counter drones is economically unsustainable.

Part 3: Classification and IFF Remains Unsolved

The Classification Challenge

Both Ukrainian and Russian forces face continuous challenge: distinguishing civilian drones from military threats and identifying specific drone types to support tactical decision-making. A quadcopter at 1,000 meters could be civilian surveillance, military reconnaissance, or configured for explosive delivery. Engagement decisions depend on rapid classification.

Ukraine has developed procedural workarounds: declaring airspace closed to all aircraft, assuming any detected drone is hostile (shoot-on-sight policies in combat zones). But even with shoot-on-sight policies, classification failures occur, resulting in engagement of non-threat or low-threat aircraft.

Why Classification Fails

Signature Ambiguity: Most drones generate nearly identical radar and optical signatures regardless of intended use. A quadcopter airframe is identical whether equipped with a camera, explosives, or RF spoofing transmitter. Classification must rely on behavioral indicators (flight pattern, altitude profile, operational context) rather than inherent signatures.

Real-Time Processing Constraints: Effective classification requires detecting, tracking, and analyzing aircraft within seconds. This compressed timeline limits the depth of analysis possible. Operator training and pattern recognition become critical but inconsistent factors.

Spoofing and Deception: Adversary drones can mimic civilian flight patterns, operate at civilian altitudes, and use frequency patterns consistent with civilian systems. Distinguishing genuine civilian drones from sophisticated deception is difficult without ground truth information (operator identity, launch location, command structure).

Implications for Rules of Engagement

Ukraine operates under combat rules of engagement that largely eliminate classification burden: declared hostilities, defined enemy identification, shoot-on-sight policies in defined military zones. These ROE are not applicable to U.S. military operations or homeland defense.

U.S. military operations over allied territory require civilian protection and proportionality assessments. U.S. homeland defense requires distinguishing genuine threats from legitimate civilian drone operations. Critical infrastructure protection requires engagement decision-making without combat ROE clarity.

Classification failures create two types of risk:

1. False Negatives: Failing to classify a threat drone, resulting in attack on U.S. forces or critical assets.
2. False Positives: Engaging civilian drones, creating international incidents, legal liability, or domestic political backlash.

Neither risk is acceptable. Yet Ukraine's three-year experience demonstrates that classification remains unsolved at operational scale.

Procurement Implications

Organizations procuring C-UAS systems should recognize that classification capability is immature. Vendor claims on "threat identification" or "automated classification" should be evaluated skeptically. Testing should include:

• False positive rates under realistic operational scenarios
• Classification latency (time from detection to classification confidence)
• Operator training requirements for classification decision-making
• Legal review of engagement decision procedures

For U.S. military and homeland defense, classification challenges imply that fully autonomous engagement (without human decision-making) remains legally and operationally unacceptable. Human-in-the-loop engagement is required.

Part 4: Adaptation Cycles Measured in Weeks, Not Years

Threat Evolution Pace

Ukraine has observed continuous evolution of drone threat profiles:

FPV Attack Drones: In 2022, FPV drones (first-person-view, pilot-guided aircraft equipped with explosives) were novel. By 2023, FPV drones had become the dominant attack platform. By 2025, both Ukrainian and Russian forces employ FPV drones in multi-drone coordinated attacks with supporting reconnaissance and electronic warfare.

Autonomous Navigation: Early Ukrainian drones relied on pilot control via radio link. By 2024, autonomous navigation (using onboard GPS, optical flow, or inertial measurement) became standard. This evolution occurred within months of EW jamming becoming widespread—autonomous navigation is a direct counter to RF jamming.

Swarm Coordination: In 2023, drone swarms were rare. By 2024-2025, swarm attacks with 10-30 simultaneous aircraft became routine. Coordination mechanisms evolved from ground-based radio control to autonomous swarm algorithms.

Payload Specialization: Early drones carried generic payloads. Current drones include specialized designs: light reconnaissance (5-minute endurance, long-range optical), heavy strike (10+ kg payload), and electronic warfare (transmitters for frequency spoofing or jamming).

Why Adaptation Cycles Are Fast

Mature Supply Chains: Drones are built from commercial components: frames, flight control boards, cameras, batteries. Adaptation requires software updates or minor hardware changes, not new component design. A software update or new design configuration can be tested and deployed within weeks.

Iterative Design: Drone development does not require years-long procurement cycles or government approval. Ukrainian and Russian forces iterate continuously, testing new configurations, measuring performance, and rapidly propagating successful designs.

Scale and Attrition: Both sides build drones in large numbers (thousands per month). This scale enables experimentation—if one design variant fails, the next variant is already in production. Rapid learning and iteration result from continuous operational feedback.

Procurement Model Mismatch

U.S. military and defense contractor procurement operates on fundamentally different timescales. A new air defense system requires:

1. Requirement Definition: 6-12 months
2. Design and Development: 12-24 months
3. Test and Evaluation: 12-18 months
4. Procurement and Production: 12-36 months
5. Fielding and Training: 6-12 months

Total timeline from requirement to operational capability: 3-7 years.

Ukrainian adaptation occurs in weeks to months. By the time a U.S. air defense system is fielded to counter one threat, that threat has evolved and countermeasures are already deployed.

Implications for Procurement Strategy

Software Updateability is Non-Negotiable: Organizations procuring C-UAS systems must prioritize systems capable of rapid software updates without requiring field modification, recertification, or extensive retraining. Systems designed for annual updates or multi-year development cycles are incompatible with threat evolution pace.

Modular Architecture: Monolithic systems that integrate hardware and software tightly create deployment barriers to updates. Modular systems (separable sensor, classification, engagement functions) enable swapping components or updating individual modules without system-wide recertification.

Operator Training Agility: If software and threat profiles change monthly, operator training must be continuous and distributed. Expecting annual training cycles or static threat briefings is insufficient. Organizations should build continuous learning and adaptation into operator culture.

Performance Baselines: Organizations should establish realistic performance baselines and accept that adversary adaptation will degrade performance over time. A system achieving 70% effectiveness at deployment will likely achieve 50% effectiveness within 12 months as adversary countermeasures mature. Procurement strategy should plan for performance degradation, not assume static capability.

Part 5: FPV Drones as the Dominant Threat Type

FPV Dominance in Ukraine

FPV (first-person-view) drones—piloted aircraft with forward-facing cameras streaming video to operators—have become the single largest source of casualties and attrition in Ukraine. These are not military-grade unmanned systems designed by defense contractors. They are modified commercial quadcopters or custom-built airframes equipped with:

• A flight control board (often commercial autopilot)
• A video camera and transmitter (streaming 5.8 GHz analog video)
• A radio receiver (receiving pilot commands on 2.4 GHz or proprietary ISM bands)
• An explosive payload (hand grenades, artillery shells, or shaped charges)

FPV drones cost $300 to $1,000 to build. They are built in garage workshops by operators with basic electrical skills. They are deployed in tens of thousands across both sides of the conflict.

Why FPV Drones Are Effective

Maneuverability: FPV drones are intentionally designed for high maneuverability. Pilots with training can execute acrobatic maneuvers, rapid altitude changes, and evasive flight patterns that exceed the pursuit capability of traditional air defense systems. A Gepard or Vulcan air defense system designed to track manned aircraft struggles to track erratic FPV drone flight.

Precision Engagement: Unlike missiles or unguided ordnance, FPV drones enable real-time aiming. The pilot can see the target and adjust flight path to impact the target precisely. This precision is particularly valuable against mobile targets (vehicles, personnel) or high-value stationary targets (command posts, equipment).

Low Cost: At $300-$1,000 per unit, FPV drones are expendable. An operation that destroys 10 FPV drones but eliminates a company-sized military unit (25-40 personnel) or destroys an air defense system is considered successful. Cost asymmetry favors drone deployment.

Pilot Skill Premium: FPV effectiveness depends heavily on operator skill. Experienced pilots can achieve 80%+ hit rates against stationary targets and 50%+ hit rates against moving vehicles. Novice pilots achieve much lower effectiveness. This creates strong incentive for operator training and concentration of experienced pilots in high-value units.

Air Defense Challenges Against FPV

Traditional air defense systems (Vulcan, Gepard, Patriot) are designed to track and engage manned aircraft or cruise missiles. FPV drones present unique challenges:

Signature Mismatch: Air defense radar designed for aircraft (1,000+ kg, 10+ meter wingspan) struggles to track drones (1-2 kg, 1-2 meter wingspan). Radar cross-section is much smaller, velocity profile is unusual (capable of near-zero velocity or rapid acceleration), and flight envelope is different.

Operator in the Loop: FPV drones are pilot-guided, not autonomous. The operator is remotely located, often kilometers away. The operator can see the target, adjust the flight path, and make real-time tactical decisions. This human-in-the-loop guidance makes FPV drones unpredictable and difficult to counter with automated systems.

Saturation Tactics: Operators deploy FPV drones in coordination with reconnaissance drones, other FPV drones, and supporting assets (electronic warfare, traditional unmanned systems). Air defense systems must simultaneously defend against multiple threat types, dividing engagement capacity.

Applicability to U.S. Threat Assessment

FPV as Low-Cost Asymmetric Threat: The FPV drone model—inexpensive, pilot-controlled, precision-guided—is directly applicable to potential U.S. adversaries. Iranian, Chinese, Russian, or North Korean forces could employ thousands of low-cost FPV drones against U.S. military formations or critical infrastructure.

Traditional Air Defense Inadequacy: Existing U.S. air defense systems (Patriot, THAAD, Avenger) were designed for manned aircraft and cruise missiles. They are not optimized for FPV drone engagement. Procurement strategy must either:

1. Modernize existing systems with new radar and guidance optimized for small, slow, maneuvering targets
2. Deploy dedicated C-UAS systems focused on drone-specific threats
3. Integrate layered defense combining multiple C-UAS modalities for drone-specific threats

Critical Infrastructure Vulnerability: FPV drones represent an emerging threat to critical infrastructure (power, communications, water). A malicious actor with commercial drone components and basic electronics skills could build FPV drones to target substations, towers, or other high-value infrastructure. Current critical infrastructure defenses do not adequately account for this threat.

Part 6: Autonomous Navigation Emerging as Counter to Jamming

Evolution to Autonomous Drones

Early Ukrainian and Russian drones relied on continuous pilot control via radio link. As RF jamming became widespread and effective, forced an operational shift. Both sides accelerated development and deployment of autonomous navigation systems enabling drones to operate without continuous radio link.

Current autonomous systems use:

• GPS/GNSS Navigation: Drones navigate to predetermined waypoints using satellite positioning. This approach is vulnerable to GPS spoofing but less vulnerable to broadband RF jamming than radio-control signals.
• Optical Flow Navigation: Drones equipped with downward-facing cameras can estimate ground velocity and maintain flight path without GPS. This approach is robust to RF jamming and spoofing.
• Inertial Measurement: Drones equipped with accelerometers and gyroscopes can estimate velocity and position drift, enabling basic autonomous flight even without external references.

Ukrainian forces have deployed commercial drones (DJI Matrice, Air2S) with autonomous waypoint navigation. Russian forces have retrofitted commercial drones with onboard autonomous systems. Both sides have developed or integrated custom autonomous software.

Implications for EW Effectiveness

Autonomous navigation directly counters RF-based electronic warfare. A jammed control link does not degrade autonomous operation if the drone can navigate to target using GPS/optical flow. This limits EW effectiveness in future conflicts.

Adversary drones are likely to combine autonomous flight with autonomous target recognition (onboard AI-based image processing to identify targets). This architecture enables fully autonomous operations from launch to engagement without communication link.

Procurement Implications

EW Limitations: Organizations relying on jamming for C-UAS must recognize that autonomous drones will progressively defeat EW countermeasures. Jamming remains effective against legacy systems but will be insufficient against mature autonomous systems.

Multi-Modality Requirement: Defense against autonomous drones requires multiple C-UAS modalities working in coordination. Optical intercept, RF sensing, kinetic engagement, and directed energy must be layered to counter drones capable of fully autonomous operation.

Optical Dominance: As RF-based systems become less effective, optical detection and engagement become more critical. Procurement strategy should prioritize optical C-UAS capability as EW effectiveness degrades.

Part 7: Autonomy in C-UAS Systems

Current State of Autonomous Engagement

U.S. military policy currently prohibits fully autonomous engagement of manned or unmanned aircraft without human approval. Current systems operate under human-in-the-loop or human-on-the-loop models where engagement is approved or executed by human operators.

However, the pace of threat evolution and engagement volume (swarms of drones) is creating operational pressure for autonomous or semi-autonomous engagement. U.S. military is developing:

• Automated Detection and Classification: AI systems identifying drone threats with high confidence, enabling rapid operator decision-making
• Semi-Autonomous Engagement: Humans approve engagement targets; systems execute engagements with minimal operator input
• Autonomous Swarm Defense: Systems capable of defending against multi-drone attacks without continuous human authorization for each engagement

Operational Drivers for Autonomy

Engagement volumes in high-threat scenarios exceed human-in-the-loop processing capacity. A swarm of 20 drones attacking simultaneously requires decisions and engagement execution faster than human operators can process. Autonomous or semi-autonomous systems are required.

Procurement and Policy Implications

Organizations procuring C-UAS systems should anticipate evolution toward autonomous engagement. Current policy constraints may evolve as threat sophistication and volume increase. Procurement strategy should:

1. Build autonomy-compatible architecture: Systems designed to enable automation without requiring hardware changes
2. Plan for policy evolution: Assume that engagement authorities may expand to include semi-autonomous systems within 5-10 years
3. Establish ROE flexibility: Operational protocols should accommodate increasing levels of autonomy as approved by policy

For critical infrastructure and private operators, autonomy raises legal and liability questions that are not yet resolved. Organizations should consult legal counsel on autonomous engagement authority before procurement.

Synthesis: Procurement Strategy for Counter-Drone

Ukrainian operational lessons inform the following procurement guidance:

For Military Operators

1. Prioritize Layered Multi-Modality Defense: EW + Optical + Kinetic, each addressing strengths of other modalities
2. Invest in Software Updateability: Threat adaptation cycles demand rapid capability updates
3. Develop Cost-Sustainable Engagement: Low-cost intercept (EW, optical) to conserve expensive kinetic resources
4. Build Autonomous Engagement Capability: Human-in-the-loop is insufficient for swarm defense; plan for autonomous/semi-autonomous systems
5. Optimize for FPV Threat: Dedicate capability to defeating maneuvering, pilot-guided drones in addition to autonomous threats

For Critical Infrastructure Operators

1. Recognize Detection-Only Limitations: Detection without mitigation authority creates incomplete defense; plan accordingly
2. Integrate with Law Enforcement: Response to detected threats depends on law enforcement availability; establish coordination protocols
3. Plan for Sustained Operations: Detection systems will operate continuously; budget for 24/7 monitoring and personnel
4. Build Organizational Resilience: If C-UAS fails or is overwhelmed, critical infrastructure must degrade gracefully without catastrophic failure
5. Monitor Regulatory Evolution: Private operator authority may expand; plan for future mitigation capability when legally available

For All Operators

1. Treat Threat Evolution as Continuous: Adversary adaptation cycles are measured in weeks. Procurement strategy must accommodate rapid obsolescence and update requirements.
2. Demand Vendor Commitment to Updates: Vendors must commit to rapid software/firmware updates, not annual releases
3. Avoid Monolithic Vendor Lock-In: Systems using proprietary components and closed integration create barriers to updates and adaptation
4. Invest in Personnel Training: Technology is only effective with trained operators. Budget for continuous training and skill development
5. Establish Performance Baselines and Acceptance of Degradation: Capability will degrade as adversaries adapt. Plan for lower effectiveness over time.

Conclusion

Ukraine has provided an unprecedented operational laboratory for C-UAS technology and tactics. The scale of drone deployment, diversity of threat types, and pace of threat evolution have exposed limitations in current approaches and validated emerging concepts.

Key lessons are clear: EW is the workhorse C-UAS effector in contested environments but faces limitations at scale; kinetic intercept struggles with volume and cost asymmetry; classification remains unsolved; adaptation cycles measured in weeks outpace traditional procurement; FPV drones represent an emergent threat class; and autonomous navigation will progressively defeat jamming-based defenses.

Procurement strategy must accommodate these lessons. Layered defense, software updateability, cost-sustainable engagement, personnel focus, and planning for continuous adversary adaptation are essential. Organizations that treat C-UAS as a static procurement problem rather than an evolving operational challenge will find their investments rapidly obsoleted by adversary advancement.

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