DON27BZ02-DV026 TITLE: DIRECT TO PHASE II: Frequency-Based Persistent Super Intelligence Tracking and Surveillance Capability

OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)

COMPONENT TECHNOLOGY PRIORITY AREA(S): Integrated Network Systems-of-Systems;Integrated Sensing and Cyber;Trusted AI and Autonomy

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)

OBJECTIVE: Develop and demonstrate a super intelligence (SI)-driven signal processing framework that leverages high-dimensional spectral feature extraction, adaptive neural control loops, and secure human-machine teaming to detect, classify, and characterize covert and agile waveforms in complex electromagnetic environment.

DESCRIPTION: The Navy faces acute operational challenges in intercepting and characterizing covert, highly agile adversarial emissions across dense electromagnetic operational environments. Modern threat systems increasingly utilize Low Probability of Intercept and Low Probability of Detection (LPI/LPD) techniques—such as Direct-Sequence Spread Spectrum (DSSS), ultra-fast frequency hopping (UFLH), wideband Linear Frequency Modulation (LFM), Costas frequency coding, and polyphase pulse compression (e.g., Frank, P1–P4 codes)—deliberately depressing emission power densities below the thermal noise floor [Refs 1, 2]. Consequently, conventional Electronic Support Measures (ESM) and wideband radiometer/energy-detection receivers exhibit severe performance degradation, failing to detect stealth radar transmissions, covert datalinks, and distributed sensor relays. This visibility shortfall undermines the tactical sensor-to-shooter timelines foundational to Combined Joint All-Domain Command and Control (CJADC2) and Distributed Maritime Operations (DMO).

To counter advanced stealth emitters, an AI-driven signal processing framework can draw upon several modular algorithmic pathways and candidate techniques, such as:

Sub-Noise-Floor Cyclostationary Analysis: For instance, computing the spectral correlation density (SCD) or cyclic autocorrelation can expose covert signal periodicities (e.g., symbol rates, chipping sequences, and carrier modulation artifacts), isolating structured LPI/LPD waveforms from stationary thermal background noise and Gaussian interference [Refs 3, 4].

Time-Frequency Deep Learning Architectures: As an example for intra-pulse modulation recognition, raw wideband In-phase/Quadrature (I/Q) samples can be transformed into high-resolution time-frequency representations—such as Wigner-Ville Distributions (WVD) or Continuous Wavelet Transforms (CWT)—and processed by deep convolutional or recurrent neural networks (CNN/RNN) to classify polyphase or frequency-coded pulses without requiring prior waveform templates [Refs 5, 6].

Adaptive Multi-Channel De-interleaving: For high-density pulse environments, candidate approaches include reinforcement-learning-guided spectral search or adaptive filter banks to isolate agile hopping channels and separate co-channel, overlapping LPI signals across wide instantaneous bandwidths [Ref 7].

Calibrated Operator Decision Metrics: To support tactical watchstanders, Bayesian neural networks or conformal prediction methods can be applied to generate uncertainty bounds alongside extracted parameters (e.g., pulse repetition intervals, modulation bandwidths, chirp rates), providing validated, interpretable indicators of stealth emitter activity.

SWaP-Constrained Edge Optimization: Potential implementation paths for tactical platforms include neural network quantization, weight pruning, and tensor acceleration to facilitate low-latency inference on Size, Weight, and Power (SWaP)-constrained naval edge computing hardware.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVWAR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: For a Direct to Phase II topic, the Government expects that the small business will have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The following actions would be required to satisfy the requirements of Phase I:

FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic NOT solely based on work performed under prior or ongoing federally funded SBIR work) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above.

DIRECT TO PHASE II: Demonstrated the feasibility and foundational algorithm performance of an AI-driven signal processing approach for the blind detection, classification, or characterization of Low Probability of Intercept/Low Probability of Detection (LPI/LPD) waveforms in simulated or captured sub-noise-floor electromagnetic spectrum environments, establishing clear metrics for signal-to-noise ratio (SNR) thresholds, probability of detection and probability of false alarm (Pd/Pfa), classification/characterization accuracy, signal density and agility, instantaneous bandwidth, processing latency, and computational viability for tactical hardware deployment. It is advantageous—and expected in the feasibility documentation and prototype concept—to state the minimum values of these metrics that apply to the prototype demonstration. These parameters are coupled: instantaneous bandwidth, signal density/agility, and processing latency determine compute and data-rate load; SNR and Pd/Pfa determine integration time and detector thresholds; and classification/characterization accuracy depends on the detections and features obtained under that load. A single, internally consistent demonstration envelope, tied to defined test conditions (waveform family, observation interval, simulated versus captured data, and platform), provides an objective success criterion for the prototype without treating the demonstration minima as full operational requirements. Values should be supported by Phase I-type results and set at or modestly beyond what that work already achieved.

PHASE II: Develop, mature, integrate, and demonstrate a working prototype that utilizes AI-driven signal processing algorithms to provide measurable improvements in the blind detection, characterization, parameter estimation, de-interleaving, and classification of adversarial LPI/LPD waveforms under contested and congested electromagnetic spectrum conditions. Validate the proposed capability within a representative laboratory or simulated operational environment, demonstrating performance against sub-noise-floor and highly agile emitters.

(Note: Prototype demonstrations may include integration with representative software-defined radio (SDR) front-ends, open architecture signal processing frameworks, electronic warfare/electronic support (EW/ES) testbeds, or relevant tactical edge computing hardware.)

Prepare a final report that includes comprehensive performance characterization data (e.g., probability of detection, false alarm rates, parameter estimation accuracy, and latency), a platform transition strategy, deployment and SWaP-C considerations, a sustainment approach, and a commercialization plan.

Work in Phase II may become classified. Please see note in the Description.

PHASE III DUAL USE APPLICATIONS: Support transition of the developed AI-driven LPI/LPD signal detection and characterization capability to Fleet operators and associated Electronic Warfare (EW), Information Warfare (IW), and maritime surveillance Programs of Record.

Commercial organizations increasingly require the ability to detect, de-interleave, identify, and monitor low-power, transient, and covert signals across congested and noisy radio frequency (RF) environments. Technologies developed under this topic may have broad applicability in 5G/6G dynamic spectrum sharing and network optimization, commercial satellite communications interference mitigation, autonomous transportation and maritime shipping collision avoidance, commercial aviation RF monitoring, passive counter-unmanned aerial system (C-UAS) sensing, critical infrastructure perimeter surveillance, and federal/industrial spectrum management.

REFERENCES:

1. Pace, P. E. "Detecting and Classifying Low Probability of Intercept Radar (2nd ed.)." Artech House Radar Library, 2009. https://www.amazon.com/dp/1580533221?lv=shuf&channelId=500&plpRedirect=mhFallback
2. Adamy, D. L. "EW 104: Electronic Warfare Against a New Generation of Threats." Artech House, 2015. https://us.artechhouse.com/EW-104-Electronic-Warfare-Against-a-New-Generation-of-Threats-P1707.aspx
3. Gardner, W. A., Napolitano, A. and Spooner, L. "Cyclostationarity: Half a century of research." Signal Processing, 86(4), 2006, pp. 639-697. https://www.sciencedirect.com/science/article/abs/pii/S0165168405002409
4. K. Kim, I. A. Akbar, K. K. Bae, J. Um, C. M. Spooner, and J. H. Reed, "Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio," in Proc. 2nd IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), 2007, pp. 212–215. https://ieeexplore.ieee.org/document/4221497
5. O’Shea, T. J., Roy, T. and Clancy, T. C. "Over-the-air deep learning based radio signal classification." IEEE Journal of Selected Topics in Signal Processing, 12(1), 2018, pp. 168-179. https://arxiv.org/pdf/1712.04578
6. T. An and B. M. Lee, "Robust Automatic Modulation Classification in Low Signal to Noise Ratio," IEEE Access, vol. 11, pp. 7860–7872, 2023. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10024264.
7. Haykin, S. "Cognitive radio: Brain-empowered wireless communications." IEEE Journal on Selected Areas in Communications, 23(2), 2005, pp. 201-220. https://ieeexplore.ieee.org/document/1391031
8. "National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. 1993." https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

KEYWORDS: AI: Quantum-Resiliency: Frequency-Based Cryptography: Adaptive-Security: Reliability: Human-Machine Teaming

TPOC 1: Maulin Patel
maulin.b.patel2.civ@us.navy.mil

TPOC 2: Patrick Cosco
patrick.j.cosco.civ@us.navy.mil