Poster Sessions

High Fidelity Test & Evaluation of Small UAS Using a COTS DRFM Based Radar Target Generation Testbed with Micro Doppler Injection

Author: Kyra Lawrence, Keysight Technologies

The rapid growth of small, unmanned aircraft systems (sUAS) continues to push the limits of traditional Test & Evaluation (T&E) methodologies across defense, security, and air surveillance domains. Small drones present extremely low radar cross sections and highly dynamic motion profiles, while natural clutter sources (particularly birds) introduce micro-Doppler signatures that often overlap with drone rotor or propulsion harmonics. Distinguishing between natural objects and sUAS remains a critical challenge for radar, EO/IR, and sensor fusion based counter UAS systems. Conventional openair testing, however, is expensive, weather dependent, and lacks the repeatability required to validate modern detection and classification algorithms. This paper presents a commercial off the shelf (COTS) radar target generation testbed that uses advanced Digital Radio Frequency Memory (DRFM) techniques to emulate the radar response of small drones with unprecedented fidelity. By capturing, modifying, and retransmitting radar waveforms in real time, DRFM enables the precise injection of micro-Doppler effects including rotor blade modulation, blade pass signatures, and harmonic fluctuations directly into the radar processing chain. This allows evaluators to create highly controlled, physics accurate signatures that differentiate between sUAS and natural clutter sources such as birds, whose wing beat micro-Doppler characteristics differ in periodicity, harmonic content, and spectral spread. The testbed integrates DRFM based waveform regeneration with real time channel modelling to simulate motion, multipath, clutter density, and environmental conditions without requiring live flight activity. As a result, radar and RF sensing systems can be exposed to fully repeatable scenarios, ranging from isolated drone signatures to dense, ambiguous environments where sUAS and biological targets coexist. The ability to switch between synthetically injected bird micro-Doppler and sUAS rotor signatures allows for controlled evaluation of classifier robustness, false alarm rates, tracker stability, and machine learning model performance. A key advantage of this COTS based approach is its reproducibility and scalability. Digital scenarios can be replayed identically across laboratories, development cycles, and test organisations something impossible to achieve with open air trials. This enables consistent regression testing, measurement of incremental algorithm improvements, and validation against evolving UAS threat profiles. When combined with hardware in the loop (HIL) integration, the platform provides an end-to-end test environment suitable for radar, RF, sensor fusion, and command and control (C2) system evaluation. Overall, the DRFM enabled radar target generation testbed offers a high fidelity, cost effective, and operationally relevant solution for T&E across the UAS/counter UAS ecosystem. By delivering micro-Doppler accurate, repeatable, lab-controlled scenarios, this approach accelerates development cycles, reduces test cost and risk, and greatly enhances confidence in system performance across surveillance and defense applications.

Channel-Adaptive AI for UAS Swarm MANET Communications

Michael Goudy, Keysight Technologies

Tactical Unmanned Aerial System (UAS) swarms depend on mobile ad hoc networks (MANETs) to sustain command, and control across dynamic propagation channel conditions and changing mesh network topologies.  The RF channel between any two nodes changes continuously as a function of platform trajectory, altitude, geometry, and three-dimensional terrain interaction.  This paper presents a Test & Evaluation (T&E) framework for evaluating AI-driven, channel-adaptive MANET communications under defense-representative swarm operating conditions.Air-to-air RF channels between swarm nodes evolve on millisecond time scales driven by platform velocity and inter-node geometry.  A formation change that repositions two nodes by tens of meters can degrade a communications link through any number of channel conditions (shadowing, multipath, doppler) at a rate that exceeds network protocol identification and corrective routing procedures. AI-native link adaptation can predict the propagation channel state characteristics before network degradation.  The AI system can anticipate effective network routing and signal modulation to maintain connectivity through maneuvers before system failure.  This effort is focused on Channel State Prediction and MANET Link Adaptation over the emulated channel models.  The framework integrates Keysight’s PROPSIM Geometric Channel Modeling (GCM) capability to generate and replay drone-to-drone channel impulse responses related to trajectory-driven RF impairments. An NVIDIA DGX Spark hosts the AI adaptation stack, executing real-time channel state prediction and MANET link management decisions in a closed loop with the emulated channel environment.  AI-driven link adaptation is compared against a reactive MANET baseline protocol operating on the same radio hardware under identical emulated channel conditions.Transit and formation-change trajectory scenarios are created to demonstrate that AI-driven link adaptation maintains higher data throughput and lower communication link failure rates. The T&E framework’s reproducible channel models, hardware-in-the-loop capabilities, and representative inference platform are intended to allow rigorous evaluation of AI-native tactical communications systems.  This framework should additionally allow extensions to electronic warfare and jamming  scenarios.

FireMAST: a deployable large scale thermomechanical fire testing capability for defence materials

Emmajane Erskine, Dstl

Delivering robust, relevant evidence on the behaviour of defence materials and structural components exposed to fire and other thermal hazards remains a challenge for Test and Evaluation (T&E). Conventional small scale fire tests often decouple heating from mechanical loading, while large, fixed facilities are scarce, extremely costly and difficult to access. As a result, there is a gap between coupon (bench) scale material data and the conditions experienced by loaded structures on real platforms, limiting the confidence of risk balance decisions on material selection, their integration and through life management. Fire Interaction and Response Evaluation: Mobile Actuated Stress Test (FireMAST) has been developed to address this gap as a deployable, containerised rig for large scale thermo-mechanical testing under controlled thermal loading, including fire  exposures. Built into a modified 20 ft ISO container, FireMAST integrates a structural reaction frame, hydraulic loading system and propane‑fired radiant panel array within a single transportable test cell, together with extraction, safety systems and segregated control room. FireMAST has been designed in partnership between Dstl and the University of Edinburgh’s Fire Research Centre (EFRC) to meet specific defence T&E needs.The reaction frame and actuator apply controlled bending loads to metre scale specimens, while the radiant panel array delivers calibrated heat fluxes up to that representative of severe thermal or fire exposure (up to 60 kW/m² at 40 mm stand‑off, increasing at reduced separation). FireMAST records synchronised load, displacement and temperature data for model development and validation, and its containerised format, based on standard freight boundaries, allows it to be relocated between sites to support on site trials or integration with existing test ranges.The poster will summarise the key design choices behind FireMAST and show how the rig has been commissioned to operate safely across its mechanical loading and thermal exposure ranges. It will also present some early data from initial tests on representative structural specimens, demonstrating how the facility can capture coupled mechanical and thermal response for subsequent model development and validation.By providing a mobile, instrumented test environment for combined thermal and mechanical loading, FireMAST extends T&E beyond fixed facilities and supports faster, more agile assessment of new materials and configurations for future platforms.

Developing UK MOD’s Range of the Future: Enabling the Evolution of Next‑Generation T&E Capabilities

Bex White, Dstl

UK Test and Evaluation (T&E) is undergoing a period of rapid transformation, driven by the need to deliver defence capability at increased pace while accommodating emerging technologies, new operating concepts, and growing system complexity. In response to these challenges, the National Armaments Director (NAD) group has articulated an aspiration for a Range of the Future that enables the rapid development, integration, and assurance of next‑generation defence capabilities. The Defence Science and Technology Laboratory (Dstl), on behalf of wider UK MOD, is delivering this ambition by evolving UK range capabilities to support next‑generation T&E needs. This work focuses on assessing novel and disruptive technologies, understanding their impact on existing T&E paradigms, and identifying where new infrastructure, processes, and services are required. Activity spans physical (live) test infrastructure, digital engineering and data‑driven services, and the increased integration of virtual, live, and synthetic environments to create more responsive and scalable test capability. This poster provides an update on progress made over the past year in developing the UK MOD Range of the Future aspiration. It highlights practical examples of how emerging technologies are being explored and assessed, the benefits they offer to modern T&E programmes, and the challenges associated with their adoption. The poster will also outline how this work aligns with, and informs, the forward programme of activity needed to realise the longer‑term Range of the Future vision. Drawing on recent MOD range activity and Dstl’s experience in evaluating emergent technologies, the poster aims to contribute to the 2026 UK ITEA Summit conference theme by sharing lessons learned and demonstrating how established aspirations are being translated into delivered capability. It also seeks to raise awareness of technology convergence and its potential to rapidly reshape infrastructure, digital services, and the future T&E enterprise.