Adaptive AI-Driven Waveform Design

Modern radar operations face unprecedented challenges in increasingly congested and contested environments. Traditional radar systems rely on fixed or pre-programmed waveform libraries, making their transmissions highly predictable and susceptible to sophisticated electronic attack (EA). Near-peer adversaries now deploy advanced cognitive jammers and digital radio frequency memory (DRFM) systems that can rapidly analyze incoming radar pulses and generate deceptive or noise-based interference. Furthermore, the rapid proliferation of both commercial and military emitters introduces significant unattended Radio Frequency Interference (RFI) that further degrades system performance. To maintain spectrum dominance and ensure reliable target detection, the next generation of radar systems must move beyond deterministic programming and embrace fully cognitive, AI-driven adaptability. Adaptive waveform design leverages advanced machine learning techniques, particularly deep reinforcement learning (DRL), to autonomously select, synthesize, and optimize transmit waveforms on the fly. By treating radar waveform design as a sequential decision-making process, a neural agent can continuously interact with the electromagnetic environment. The agent ingests real-time spectral observations—such as interference patterns and target returns—and actions it by adjusting key waveform parameters. These parameters include pulse repetition frequency (PRF), pulse width, bandwidth, modulation type (e.g., non-linear frequency modulation, polyphase coding), and frequency hopping schemes. Implementing this capability requires bridging the gap between high-level AI algorithms and low-latency, real-time hardware execution. The neural network inference must execute within micro-seconds to alter parameters on a pulse-to-pulse or coherent processing interval (CPI) basis. Therefore, the developed models must be highly optimized for deployment on edge-computing architectures, such as software-defined radios (SDRs) backed by field-programmable gate arrays (FPGAs) or specialized system-on-chip (SoC) processors, ensuring high performance without exceeding strict Size, Weight, and Power (SWaP) constraints. Phase I consists of researching algorithms (e.g. Deep Q-Network or policy gradient RL) that ingest real or simulated sensor data (e.g. jamming signatures, clutter maps) and output waveform properties/design (frequency, bandwidth, etc.). A prototype would show improved detection vs. fixed waveforms in a variety of simulated clutter and jamming environments. In Phase II, the system would be implemented in small-SWaP radar hardware (leveraging SDR and FPGA capabilities) to adapt waveforms in real-time. Phase III would integrate the adaptive waveform engine into Army radars (e.g. airborne or UAS platforms) to improve anti-jam resilience. Because this capability could also benefit commercial radar or comms (e.g. automotive radar avoiding interference, dynamic spectrum sharing radios), it has dual-use potential.