Combat Aircraft Swarm Radars Market

Combat Aircraft Swarm Radars Market Forecast and Outlook 2026 to 2036

The global market for combat aircraft radars designed to counter drone swarms is evolving rapidly, projected to grow from USD 2.21 billion in 2026 to USD 4.60 billion by 2036, at a 7.6% CAGR.

Key Takeaways from the Combat Aircraft Swarm Radars Market

• Market Value for 2026: USD 2.21 Billion
• Market Value for 2036: USD 4.60 Billion
• Forecast CAGR (2026-2036): 7.6%
• Leading Radar Type Segment (2026): Active Electronically Scanned Array (AESA) (44%)
• Leading Application Segment (2026): Swarm Detection & Counter-UAS (52%)
• Key Growth Countries: India (8.90% CAGR), China (8.20% CAGR), USA (6.90% CAGR), UK (7.00% CAGR), Germany (6.60% CAGR)
• Key Players in the Market: Lockheed Martin Corporation, Northrop Grumman, Raytheon Technologies (RTX), BAE Systems, Thales Group

Growth is driven by the urgent tactical shift from traditional air dominance to defending against asymmetric, low-cost swarm threats. Modern radars are no longer just for tracking individual high-value targets; they must detect, classify, and prioritize dozens of small, low-flying drones simultaneously.

The core trend is the integration of artificial intelligence and machine learning directly into the radar's processing chain. AI enables real-time discrimination of swarm members from clutter, predicts swarm behavior, and assigns threat levels to guide kinetic or electronic countermeasures. This capability is becoming a critical differentiator for next-generation fighter aircraft and upgrade programs.

Development is focused on achieving greater power efficiency, wider scan volumes, and lower probability of intercept to maintain aircraft stealth while managing the swarm threat. The transition is from hardware-defined systems to software-defined, updateable sensors where new threat libraries and AI models can be deployed to counter evolving swarm tactics.

India leads growth with an 8.90% CAGR, driven by its active border security challenges and ambitious indigenous fighter program. China follows at 8.20%, focusing on integrating advanced swarm detection into its expanding fleet of stealth aircraft. Markets in the US and Europe are characterized by upgrades to existing platforms like the F-35, Eurofighter, and Rafale to maintain technological overmatch.

Metric

Metric	Value
Market Value (2026)	USD 2.21 Billion
Market Forecast Value (2036)	USD 4.60 Billion
Forecast CAGR (2026-2036)	7.6%

Category

Category	Segments
Radar Type	Active Electronically Scanned Array (AESA), Passive Electronically Scanned Array (PESA), Hybrid AESA-PESA, Phased Array with AI-Assisted Swarm Detection, Others
Application	Swarm Detection & Counter-UAS, Air-to-Air Target Tracking, Terrain Following & Obstacle Avoidance, Electronic Warfare Support
Region	North America, Latin America, Western Europe, Eastern Europe, East Asia, South Asia & Pacific, MEA

Segmental Analysis

By Radar Type, Which Technology is Foundational?

Active electronically scanned array (AESA) radar holds a dominant 44% share. Its solid-state, software-defined architecture is ideally suited for swarm warfare. AESA provides the agile, multi-mode beam steering needed to simultaneously track a high volume of targets while performing other functions like communications or electronic attack. Its reliability and resistance to jamming make it the indispensable core technology upon which advanced AI-driven swarm detection algorithms are built.

By Application, Which Mission is Defining Demand?

Swarm detection & counter-UAS is the defining application, commanding a 52% share. This reflects the paradigm shift in air combat priorities. The technical challenge of distinguishing a small drone from a bird or ground clutter, and then tracking hundreds of such objects in a coordinated swarm, is the primary driver for radar software and processing upgrades across both new and legacy combat aircraft platforms.

What are the Drivers, Restraints, and Key Trends of the Combat Aircraft Swarm Radars Market?

The proliferation of commercially available drone technology and the demonstrated tactical threat of drone swarms in recent conflicts. This has created an immediate and unambiguous requirement for air platforms to defend themselves and high-value assets from saturation attacks.

The extreme technical difficulty and associated cost of developing and integrating AI/ML processors that can operate in the size, weight, and power-constrained environment of a fighter jet’s radar, while meeting the rigorous safety and certification standards of military aviation.

The move towards multi-function RF systems where the AESA radar acts as a central hub not only for sensing but also for electronic warfare (jamming, deception) and secure datalink communications, creating a unified "sense-and-effector" against swarms.

Analysis of the Market by Key Countries

Country	CAGR (2026-2036)
India	8.90%
China	8.20%
UK	7.00%
USA	6.90%
Germany	6.60%

What is Driving India's Investment in Indigenous Swarm Defense Capabilities?

India's leading 8.90% CAGR is fueled by a direct response to regional security dynamics and a strategic push for self-reliance. The imperative to protect airspace from potential saturation attacks drives significant investment.

This focus is channeled into two primary streams: outfitting the indigenous TEJAS MK-1A and upcoming MK-2 fighters with advanced, domestically developed AESA radars featuring AI-driven swarm processing, and pursuing comprehensive radar upgrade programs for its sizable fleet of Sukhoi and Mirage aircraft, often through technology-sharing partnerships with global OEMs.

How is China Integrating Swarm Detection Across Its Modernizing Fighter Fleet?

China's 8.20% growth stems from a systematic, network-centric approach to air defense. The development and integration of advanced AESA radars are synchronized across platforms like the J-20 stealth fighter, J-10C, and J-16 to create a unified sensor grid.

Domestic R&D prioritizes AI-enabled sensor fusion and data-linking, aiming to seamlessly integrate the radar picture from a single aircraft into a broader battle management system. This allows for collaborative engagement, where one platform detects a swarm and cues weapons from another, creating a resilient kill web.

Why is USA’s Market Focused on Upgrading Both Legacy and 5th-Generation Platforms?

The USA's 6.90% CAGR reflects a two-pronged strategy to maintain overwhelming technological advantage. For 5th-generation assets like the F-35, growth is driven by continuous capability updates via software, enhancing the existing APG-81 radar's swarm discrimination algorithms.

Concurrently, significant investment flows into upgrading 4th-generation platforms like the F-15EX and F-16 with new-generation AESA radars (e.g., APG-82, APG-83). These upgrades are designed with open-system architectures, allowing for future AI processor inserts to counter evolving swarm tactics without a full hardware replacement.

What Role does the UK Play in Consortium-Based Radar Modernization?

The UK's 7.00% growth is deeply tied to its leadership in multinational European defense programs. The core of its market activity involves the ongoing Captor-E AESA radar integration onto the Eurofighter Typhoon, which includes specific software development for swarm detection.

The UK is a pivotal partner in the Future Combat Air System (FCAS/GCAP) program, where it collaborates with Italy and Japan to define and develop the next-generation radar system, with multifunction swarm defense as a foundational operational requirement from the outset.

How is Germany's Expertise Shaping Next-Generation European Swarm Defense Radars?

Germany's 6.60% CAGR is anchored in its engineering leadership within Europe's defense industrial base. As a core member of the Eurofighter consortium, German industry is crucial for the development and integration of advanced radar modes for the Captor-E system.

Germany's market influence is concentrated on the FCAS program, where its expertise in sensor technology, electronic warfare, and systems integration is directed toward creating a revolutionary System of Systems approach. The focus is on developing a radar that functions not merely as a sensor, but as the central node for electromagnetic spectrum dominance against swarm threats.

Competitive Landscape of the Combat Aircraft Swarm Radars Market

Established defense prime contractors who serve as system integrators for major fighter programs dominate the landscape. Success depends on mastery of AESA technology, AI/ML algorithm development, and the ability to miniaturize high-performance computing for airborne environments.

Strategic Recommendation	Rationale
Invest in Open, Modular Radar Architectures	Develop systems that allow for drop-in replacement of processor and AI modules to extend platform life and simplify upgrades, appealing to cost-conscious militaries.
Forge Alliances with AI Software Specialists	Partner with agile tech firms specializing in machine learning and cognitive EW to accelerate algorithm development and stay ahead of evolving swarm tactics.
Develop and Market Retrofit Solutions	Create scalable AESA back-ends and processor upgrades for legacy fighter fleets, offering a cost-effective path to enhanced swarm defense for operators not buying new jets.
Demonstrate Integration with Broader Kill Webs	Prove how the radar's tracking data seamlessly integrates with ground-based C2, missile systems, and directed energy weapons to enable multi-domain swarm defeat.

Key Players in the Combat Aircraft Swarm Radars Market

• Lockheed Martin Corporation
• Northrop Grumman
• Raytheon Technologies (RTX)
• BAE Systems
• Thales Group

Scope of Report

Items	Values
Quantitative Units	USD Billion
Radar Type	AESA, PESA, Hybrid AESA-PESA, Phased Array with AI-Assisted Swarm Detection, Others
Application	Swarm Detection & Counter-UAS, Air-to-Air Target Tracking, Terrain Following, EW Support
Key Countries	India, China, USA, UK, Germany
Key Companies	Lockheed Martin, Northrop Grumman, Raytheon Technologies, BAE Systems, Thales Group
Additional Analysis	Technical analysis of AI/ML processing requirements for real-time swarm discrimination in radar returns; comparative assessment of sensor fusion techniques (radar, EO/IR, ESM) for swarm identification; cost-benefit analysis of new-build vs. retrofit radar solutions for legacy fighter fleets; examination of cybersecurity vulnerabilities in networked, software-defined radar systems; impact of electronic warfare and low-probability-of-intercept (LPI) requirements on radar design for swarm engagement.

Market by Segments

• Radar Type :

  • Active Electronically Scanned Array (AESA)
  • Passive Electronically Scanned Array (PESA)
  • Hybrid AESA-PESA
  • Phased Array with AI-Assisted Swarm Detection
  • Others

• Application :

  • Swarm Detection & Counter-UAS
  • Air-to-Air Target Tracking
  • Terrain Following & Obstacle Avoidance
  • Electronic Warfare Support

• Region :

  • North America
    • USA
    • Canada
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Rest of Latin America
  • Western Europe
    • Germany
    • UK
    • France
    • Spain
    • Italy
    • BENELUX
    • Rest of Western Europe
  • Eastern Europe
    • Russia
    • Poland
    • Czech Republic
    • Rest of Eastern Europe
  • East Asia
    • China
    • Japan
    • South Korea
    • Rest of East Asia
  • South Asia & Pacific
    • India
    • ASEAN
    • Australia
    • Rest of South Asia & Pacific
  • MEA
    • Saudi Arabia
    • UAE
    • Turkiye
    • Rest of MEA

References

• Defence Advanced Research Projects Agency (DARPA). (2024). Algorithms for Counter-Swarm Systems (ACS). Program Information.
• Kendall, F. (2023). Department of the Air Force Posture Statement. U.S. Department of the Air Force.
• Moscow Institute of Physics and Technology. (2023). Signal Processing for Dense Target Environments in AESA Radars. Journal of Communications Technology and Electronics, 68(4).
• North Atlantic Treaty Organization (NATO). (2024). STO Lecture Series on Counter-Unmanned Aircraft Systems (C-UAS). NATO Science & Technology Organization.
• Wang, H., & Liu, Z. (2023). Cognitive Radar and Machine Learning for Autonomous Target Recognition. Artech House.