What is ALFIE?

ALFIE (AI/ML Learning Filter Integrated Engine) is a sophisticated AI/ML-based system designed to enhance signal quality and integrity in various hardware environments. By leveraging advanced neural networks and data-driven learning techniques, ALFIE dynamically adjusts to the unique requirements of different waveform types.

ALFIE compensates for component-induced signal imperfections, ensuring that validated source waveforms result in high-fidelity real-world waveforms. This capability makes ALFIE integral to the success of systems like Dynamic Cognitive Adaptive Filtering (DCAF), enabling accurate signal reproduction and calibration across diverse hardware setups.

PRIMARY ALFIE GOALS

ALFIE IS:

Improved overall solution to loss functions; better fit to signal of interests; quantitatively improved solution metrics

Training and inference times are quicker to desired solution than legacy adaptive filters

ALFIE is designed to be completely hardware agnostic; integrates into any pre-existing or future RF system

Dynamic Cognitive Adaptive Filtering (DCAF)

  • DCAF Leverages ALFIE to execute threat-specific waveform mimicry training on agnostic software defined radio platforms, converting virtual, digital-twin metadata into sophisticated RF output waveforms.

  • The topology creates two signals, one original and one with emulated hardware artifacts such as I/Q imbalance and phase distortion, which ALFIE processed to reduce artifacts, restore signal quality, and wholistically mimic systems of interest.

  • Development for optimizing training sessions and integrating DCAF with advanced hardware suite, resulting in improved performance and scalability.

Signal Mimicry

  • ALFIE recreates a signal as close to the source waveform as possible, which may involve the addition or removal of artifacts depending on the supplied signal source.

  • ALFIE's neural network configuration allows for the optimization of training sessions to adaptively manage and manipulate signal artifacts, maintaining the integrity of the desired signal.

  • With recreation of desired signal attributes, signal mimicry can be applied to a myriad of use cases including electronic attack, cyber-EA spoofing, and DRFM efficacy increases

Digital Twin Emulation

  • DCAF supports the creation of validated real-world waveforms from digital twin models to ensure accurate threat representation during testing.

  • The system transforms SPDWs into I/Q data, enabling the emulation of digital twin threat models for real-world testing scenarios.

  • DCAF's architecture allows for the integration of digital twins into fielded systems, ensuring waveform fidelity throughout the hardware chain.

  • Implementing ODESSA models within DCAF enhances simulation capabilities by enabling the execution of scenarios using engineering-fidelity models.

Cognitive Hardware Calibration

  • ALFIE uses AI/ML algorithms to adaptively filter waveforms, improving signal reproduction accuracy by learning from hardware-induced imperfections.

  • The system's cognitive filtering capabilities allow it to learn outside of pre-determined boundary conditions, solving loss functions creatively and effectively.

  • Calibration involves optimizing DDL sessions to align ALFIE's neural network configuration with the specific requirements of different hardware systems.

  • The recursive nature of DDL-produced filter algorithms enables dynamic compensation for signal differences, facilitating accurate hardware calibration and ensuring consistent signal quality across diverse hardware environments.