公开(公告)号：US20210048507A1

公开(公告)日：2021-02-18

申请号：US16539578

申请日：2019-08-13

Applicant: BAE SYSTEMS Information and Electronic Systems Integration Inc.

Inventor： Scott A. Kuzdeba ,  Amit Bhatia ,  David J. Couto ,  Denis Garagic ,  John A. Tranquilli, JR.

IPC: G01S7/02 ,  G06N20/00

Abstract: The discriminability of an RF fingerprint is increased by “abstracting,” “enhancing,” and “reconstructing” a digital signal before it is transmitted, where the abstraction is a reversible nonlinear compression, the enhancement is a modification of the abstracted data, and the reconstruction is a mapping-back of the abstraction. During a training phase, for each individual RF transmitter, RF fingerprints are analyzed and candidate enhancements are modified until a successful enhancement is identified that provides satisfactory discriminability improvement with minimal signal degradation. The successful enhancement is implemented in the RF transmitter, and the RF fingerprint is communicated to receivers for subsequent detection and verification. Reinforcement learning can direct modifications to the candidate enhancements. The abstraction can implement a deep generative model such as an auto-encoder. A covert data enhancement can encode covert data onto the RF fingerprint, whereby the covert data is transmitted covertly to a receiver.

公开(公告)号：US11378646B2

公开(公告)日：2022-07-05

申请号：US16539578

申请日：2019-08-13

Applicant: BAE SYSTEMS Information and Electronic Systems Integration Inc.

Inventor： Scott A Kuzdeba ,  Amit Bhatia ,  David J. Couto ,  Denis Garagic ,  John A. Tranquilli, Jr.

IPC: G01S5/02 ,  G01S7/02 ,  G06N20/00

Abstract: The discriminability of an RF fingerprint is increased by “abstracting,” “enhancing,” and “reconstructing” a digital signal before it is transmitted, where the abstraction is a reversible nonlinear compression, the enhancement is a modification of the abstracted data, and the reconstruction is a mapping-back of the abstraction. During a training phase, for each individual RF transmitter, RF fingerprints are analyzed and candidate enhancements are modified until a successful enhancement is identified that provides satisfactory discriminability improvement with minimal signal degradation. The successful enhancement is implemented in the RF transmitter, and the RF fingerprint is communicated to receivers for subsequent detection and verification. Reinforcement learning can direct modifications to the candidate enhancements. The abstraction can implement a deep generative model such as an auto-encoder. A covert data enhancement can encode covert data onto the RF fingerprint, whereby the covert data is transmitted covertly to a receiver.

公开(公告)号：US11087228B2

公开(公告)日：2021-08-10

申请号：US15235879

申请日：2016-08-12

Applicant: BAE Systems Information and Electronic Systems Integration Inc.

Inventor： Denis Garagic ,  Bradley J Rhodes

IPC: G06N7/00

Abstract: A generic online, probabilistic, approximate computational inference model for learning-based data processing is presented. The model includes detection, feature production and classification steps. It employs Bayesian Probabilistic Models (BPMs) to characterize complex real-world behaviors under uncertainty. The BPM learning is incremental. Online learning enables BPM adaptation to new data. The available data drives BPM complexity (e.g., number of states) accommodating spatial and temporal ambiguities, occlusions, environmental clutter, and large inter-domain data variability. Generic Sequential Bayesian Inference (GSBI) efficiently operates over BPMs to process streaming or forensic data. Deep Belief Networks (DBNs) learn feature representations from data. Examples include model applications for streaming imagery (e.g., video) and automatic target recognition (ATR).