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公开(公告)号：US20220163626A1
公开(公告)日：2022-05-26
申请号：US16953562
申请日：2020-11-20
Inventor： Scott A. Kuzdeba , Brandon P. Hombs , Peter J. Kajenski , Daniel Massar
Abstract: One or more defined countermeasures are selected from a countermeasure library, populated with parameters, and applied against an unknown, ambiguous, or unresponsive imminent radar threat based on an analysis of a hostile RF waveform emitted by the radar threat. The analysis can include comparing static and/or dynamic features of the hostile RF waveform with features of known hostile RF waveforms. A parameter set associated with the selected defined countermeasure in the countermeasure library can be selected. Waveform features can be categorized and sub-categorized for comparison with the known hostile waveforms. A plurality of features can be detected and compared. The analysis can include correlating behavior patterns of a plurality of hostile RF waveforms emitted by the radar threat. A cognitive intelligence trained using a threat database and library of corresponding countermeasures can analyze the hostile RF waveform, select the defined countermeasure, and/or select or generate the parameters.
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公开(公告)号：US20230130863A1
公开(公告)日：2023-04-27
申请号：US17358153
申请日：2021-06-25
Inventor： Scott A. Kuzdeba , Joseph M. Carmack , James M. Stankowicz, JR.
IPC: G06N3/0464 , G06N3/063
Abstract: A Deep-Learning (DL) system for representation and construction or reconstruction of signals includes an encoder stage; an encoding; an optional modification stage; a decoder stage, and a (re)construction stage. The encoder stage includes layers of dilated convolutions, and the encoder maps from an input representation into a latent embedded representation. It learns a set of features that encode the input signals. The encoding stage comprises latent space; the decoder stage maps from latent features back to an output of the same size as the input signals, whereby the output has the same dimensionality and representation as the input signals. Modification to the signal can be conducted within the latent representation to alter the (re)construction for specific tasks, such as increasing a device's RF fingerprint.
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公开(公告)号：US20140036103A1
公开(公告)日：2014-02-06
申请号：US13952777
申请日：2013-07-29
Inventor： Scott A. Kuzdeba , Brandon P. Hombs , Alexander M. Wyglinski
IPC: H04N5/232
CPC classification number: H04N5/23293 , G06K7/1095 , G06K7/14 , G06K19/06112 , H04N7/002 , H04N7/025
Abstract: A system and method for platform independent LOS visual information transmission is disclosed. A transmitter consists of a series of sequential images that are stacked together to form frames in a video transmission. Each image is modulated spatially, by color, and by intensity. The data is transmitted over an LOS visual channel. The receiver first captures each individual image from the received video, and then demodulates each image in the three areas it was modulated spatially, by color, and by intensity. LOS visual information transmission allows for secure data transfer and reduces interference from other applications.
Abstract translation: 公开了一种用于平台独立的LOS视觉信息传输的系统和方法。 发射机由一系列连续的图像组成，这些图像被堆叠在一起以在视频传输中形成帧。 每个图像在空间上，颜色和强度调制。 数据通过LOS视频通道传输。 接收机首先从接收到的视频捕获每个单独的图像，然后解调在空间上，通过颜色和强度调制的三个区域中的每个图像。 LOS视觉信息传输允许安全的数据传输，并减少其他应用的干扰。
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Patent Agency Ranking
公开(公告)号：US09167165B2
公开(公告)日：2015-10-20
申请号：US13952777
申请日：2013-07-29
Inventor： Scott A. Kuzdeba , Brandon P. Hombs , Alexander M. Wyglinski
CPC classification number: H04N5/23293 , G06K7/1095 , G06K7/14 , G06K19/06112 , H04N7/002 , H04N7/025
Abstract: A system and method for platform independent LOS visual information transmission is disclosed. A transmitter consists of a series of sequential images that are stacked together to form frames in a video transmission. Each image is modulated spatially, by color, and by intensity. The data is transmitted over an LOS visual channel. The receiver first captures each individual image from the received video, and then demodulates each image in the three areas it was modulated spatially, by color, and by intensity. LOS visual information transmission allows for secure data transfer and reduces interference from other applications.
Abstract translation: 公开了一种用于平台独立的LOS视觉信息传输的系统和方法。 发射机由一系列连续的图像组成，这些图像被堆叠在一起以在视频传输中形成帧。 每个图像在空间上，颜色和强度调制。 数据通过LOS视频通道传输。 接收机首先从接收到的视频捕获每个单独的图像，然后解调在空间上，通过颜色和强度调制的三个区域中的每个图像。 LOS视觉信息传输允许安全的数据传输，并减少其他应用的干扰。
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公开(公告)号：US20220217619A1
公开(公告)日：2022-07-07
申请号：US17142800
申请日：2021-01-06
Inventor： Joshua W. Robinson , Joseph M. Carmack , Scott A. Kuzdeba , James M. Stankowicz, JR.
Abstract: A system whereby individual RF emitter devices are distinguished in real-world environments through deep-learning comprising an RF receiver for receiving RF signals from a plurality of individual devices; a preprocessor configured to produce complex-valued In-phase (I) and Quadrature (Q) IQ signal sample representations; a two-stage Augmented Dilated Causal Convolution (ADCC) network comprising a stack of dilated causal convolution layers and traditional convolutional layers configured to process I and Q components of the complex IQ samples; transfer learning comprising a classifier and a cluster embedding dense layer; unsupervised clustering whereby the RF signals are grouped according to a device that transmitted the RF signal; and an output identifying the individual RF emitter device whereby the individual RF emitter device is distinguished in the real-world environment.
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公开(公告)号：US20220163629A1
公开(公告)日：2022-05-26
申请号：US16953659
申请日：2020-11-20
Inventor： Scott A. Kuzdeba , Matthew Anderson , Brandon P. Hombs , Daniel Massar , John A. Tranquilli, JR.
Abstract: A closed loop, real-time, cognitive Electronic Warfare (EW) system without a threat database includes an EW receiver for receiving radar threat signals; a Signal Analysis and Characterization module; a Pulse to Emitter Association sub-module; a Function De-interleaving Classifier sub-module; a Threat Behavior Model sub-module; a Countermeasures Synthesis module; a Capability, Severity, and Intent sub-module; a Countermeasure Selection sub-module; a Countermeasure Optimization sub-module; a Countermeasures Effectiveness Assessment module; a Resource Management module; and an EW transmitter.
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公开(公告)号：US20220163627A1
公开(公告)日：2022-05-26
申请号：US16953568
申请日：2020-11-20
Inventor： Scott A. Kuzdeba , Brandon P. Hombs , Peter J. Kajenski , Daniel Massar
Abstract: A method of selecting and optimizing a countermeasure for application against a novel, ambiguous, or unresponsive radar threat includes selecting a candidate countermeasure and an initial parameter set and varying at least one of the parameters while the effectiveness of the candidate countermeasure against the radar threat is assessed, for example by a human observer. Embodiments include repeating the process with additional candidate countermeasures. For an unresponsive radar threat, a previously effective countermeasure can be selected as the candidate countermeasure. For an ambiguous radar threat, at least one countermeasure previously verified as effective against a partially matching known threat can be selected as the candidate countermeasure. Correlated parameters can be simultaneously varied. An optimization surface and trajectory formed by a plurality of correlated parameters can be identified by machine intelligence, used to guide the parameter variations, and/or stored for use against the same or similar threats in the future.
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公开(公告)号：US20210048507A1
公开(公告)日：2021-02-18
申请号：US16539578
申请日：2019-08-13
Inventor： Scott A. Kuzdeba , Amit Bhatia , David J. Couto , Denis Garagic , John A. Tranquilli, JR.
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.
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