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Multidomain Sensors Demonstrator and Test for Cognitive Radar Against Hypersonic and UAV Threats

This European Defence Fund (EDF) 2026 Research Action topic (“Multidomain sensors demonstrator and test”) targets the development and demonstration of an innovative, adaptive radar sensor able to detect, track, and classify both hypersonic glide vehicle (HGV) threats and unmanned aerial vehicles (UAVs) in complex, dynamic environments. The call is motivated by the combination of very high speed, significant manoeuvrability, and changing radar cross section (RCS) characteristics of hypersonic threats, as well as the rapidly expanding operational use of UAVs (including mini- and micro-UAVs) for reconnaissance and intelligence. To address these challenges, the topic emphasises adaptive waveform design and signal processing, supported by innovative approaches such as artificial intelligence (AI) and cognitive radar methods, including a feedback loop between received signal characteristics and transmitted waveforms.

The project must develop and field test an engineering development model (EDM) and investigate cognitive/AI-enabled algorithms for detection and tracking, target classification, clutter cancellation, and electronic counter-countermeasures (ECCM). This includes research on HGV and UAV characteristics relevant to radar detection/tracking/classification, and research on models enabling real-time adaptation of radar waveforms and processing to changing RCS during high-speed manoeuvres. A dedicated focus is placed on databases that support cognitive approaches and signature data preparation (hypersonic, mini- and micro-UAV), including analysis of latency and options for remote database use versus edge processing.

Beyond generating knowledge, proposals must integrate knowledge by analysing the interaction between active electronically scanned array (AESA) concepts and the cognitive radar module. Mandatory studies include defining future sensor concepts (with cognitive capabilities), producing requirement specifications for the targeted sensor and the EDM, and creating a roadmap for specialised AI algorithms and validation databases. The design and prototyping work culminates in PDR and CDR, covering an AESA antenna based on digital transmission/receive modules (TRM) with SWaP-C considerations, and signal/data processing exploiting selected technologies (e.g., high-resolution range and AI). The EDM should include AESA operation (rotating and stare modes), platform mounting in a representative environment, digital beamforming and processing for baseline radar functions plus novel algorithms, the ability to inject test target signals for calibration/testing, and data recording. Near-field antenna testing is required to characterise Tx/Rx and auxiliary patterns. Field testing must cover at least two domains (e.g., ground and sea clutter). Recorded data must be examined and assessed in the final report, and a clear, feasible path to full sensor development (effort, schedule, spending profile, and recurring cost estimates) must be provided. The call also encourages synergies with prior EDF Advanced Radar Technologies topics (EDF-2021-RA-SENS-ART and EDF-2022-RA-SENS-ART). This topic contributes to the STEP objectives in the area of deep and digital technologies.

Opening: 22.01.2026
Deadline(s): 29.09.2026

Expected Outcome

- An EDM demonstrating adaptive waveform and receiver-filter selection in real time for improved detection/tracking of manoeuvring HGVs and UAVs.
- Validated cognitive/AI algorithms and associated databases enabling classification (initial classes: hypersonic vehicles, Class I/II UAVs, fighters) and clutter mitigation.
- Evidence of improved detection and tracking ranges and enhanced robustness against electronic countermeasures versus existing radar approaches.
- An integrated AESA-based, software-defined radar architecture concept with scalable digital TRM and network-oriented cyber-resilient design.
- Demonstrations in representative environments for UAV detection/tracking/classification (and analysis-based demonstration for HGVs), supported by recorded data assessment.
- A roadmap for specialised AI algorithms, validation databases, and a credible path to full sensor development including schedule, spending profile, and recurring cost estimate.
- Contributions to EU strategic autonomy by strengthening critical defence sensor technologies and reducing strategic dependencies.

Scope

- Develop and field test an engineering development model (EDM) of an innovative cognitive/adaptive radar system.
- Research HGV and UAV characteristics for detection, tracking and classification, including changing RCS and high manoeuvrability.
- Investigate real-time adaptive radar waveform and signal processing (waveform–receiver filter optimisation) using AI/cognitive methods and a feedback loop.
- Build and evaluate signature databases for hypersonic, mini- and micro-UAV targets; assess remote vs edge-processing latency constraints.
- Develop and validate AI/ML algorithms for target classification, clutter cancellation, and ECCM robustness in simulated scenarios and case studies.
- Study and demonstrate ISAR and micro-Doppler signature-based classification with novel (AI-based) approaches.
- Analyse integration between AESA concepts (digital TRM, scalable modular architecture) and the cognitive radar module.
- Complete requirements specifications and a roadmap for specialised AI algorithms and validation databases.
- Design, review (PDR/CDR), prototype, and test AESA and digital beamforming/signal-processing subsystems; conduct near-field antenna characterisation.
- Demonstrate UAV detection/tracking/classification in a representative multidomain environment (at least two domains) and demonstrate HGV capability by analysis.

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