Adaptive AI-Driven Waveform Design - SBIR Topic OSW26BZ04-DV009

Funding Amount:

Est. $314,363

Deadline to Apply:

August 19th, 2026

ITAR:

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

Objective:

Develop an AI/ML-controlled radar waveform generator that dynamically designs a waveform, including adjustments to frequency, modulation, or code in real time to optimize performance under changing conditions and to evade jamming.

Description:

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)

• 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 microseconds 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.

PHASE I:

The objective of Phase I is to conduct a feasibility study to determine the scientific, technical, and commercial merit of applying AI/ML algorithms—specifically Deep Reinforcement Learning (DRL)—to dynamic radar waveform adaptation.

Offerors are expected to develop simulated RF environments that incorporate realistic adversarial jamming, unattended Radio Frequency Interference (RFI), and clutter.

Within these scenarios, performers will train neural models to autonomously design and select waveform parameters that maximize critical metrics, such as Signal-to-Interference-plus-Noise Ratio (SINR) and target detection probability.

The desired end product is a proof-of-concept software model and a small-scale prototype that successfully validates technical feasibility by demonstrating measurably improved detection performance against traditional fixed-waveform baselines.

PHASE II:

Offerors are expected to implement the AI/ML adaptive waveform controller onto edge-computing hardware, leveraging FPGA or DSP architectures integrated with a Software-Defined Radio (SDR) front-end.

A critical expectation is the rigorous optimization of the machine learning inference models to achieve the ultra-low latency required for pulse-to-pulse or coherent processing interval (CPI) adaptation.

The resulting system must be integrated with a radar prototype and subjected to comprehensive laboratory or relevant field testing against dynamic, realistic jamming sources and complex interference.

The minimum required deliverable is a fully integrated, physical prototype of the AI-driven waveform engine that demonstrates quantifiable improvements in target detection and anti-jam performance compared to traditional fixed-waveform systems.

This prototype must be sufficiently mature to establish a clear path toward commercial viability and subsequent integration into future DoD sensor platforms.

PHASE III DUAL USE APPLICATIONS:

In Phase III, the developed technology is expected to transition into operational military systems and viable commercial products, supported by non-SBIR/STTR funding.

For DoD and military applications, the adaptive waveform engine will be integrated into existing and future Army radar and systems to enable cognitive modes that dynamically adapt to contested spectrums and complex threat environments.

Specific military applications include:

• Enhancing UAS sensors

• Ground surveillance systems

• Active Electronically Scanned Array (AESA) radars with the capability to autonomously evade advanced jammers

In the commercial sector, this technology possesses strong dual-use potential for advanced automotive radars (e.g., autonomous vehicle navigation) and civil air-traffic control systems, allowing them to actively mitigate unattended interference in dense RF environments.

Additionally, the core capability can be leveraged for dynamic spectral coexistence, enabling commercial sensors and advanced telecommunications networks to share bandwidth efficiently without mutual degradation.

Who will win?

If you can achieve the objective above better than any other company on the market, you have a very high-likelihood of success and should apply.

Who is eligible to apply?

Any company that meets the following criteria:

• For-profit company

• U.S.-owned and controlled.

• 500 or fewer employees (including affiliates)

How Can BW&CO Help?

1) End-to-end support including, strategy, writing of the full proposal, and administrative & compliance support.

2) Proposal strategy and review.

3) Administrative & compliance support.

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