Bae Systems Information and Electronic Systems Integration Inc.

Worcester Polytechnic Institute

Scott A Kuzdeba

Brandon P Hombs

Daniel Massar

John A Tranquilli, Jr

Joseph M Carmack

Joshua W Robinson

Amit Bhatia

David J Couto

Denis Garagic

Matthew Anderson

Alexander M Wyglinski

James M Stankowicz, Jr

Troy M Lau

Peter J Kajenski

Jonathan M Sussman-Fort

A method of assessing the effectiveness of an electronic countermeasure (ECM) applied against an unknown, ambiguous, or unresponsive radar threat includes monitoring changes in a radar-associated factor while applying the ECM and determining if the ECM is disrupting the hostile radar. The radar-associated factor can be a weapon that is controlled by the radar threat, and assessing the ECM can include determining whether the weapon is misdirected due to applying the ECM. Or the radar-associated factor can be a feature of an RF waveform emitted by the radar threat, and assessing the ECM can include determining if the feature is changed due to applying the ECM. Continuous changes in the feature can indicate unsuccessful attempts to mitigate the ECM. Return of the feature to a pre-threat state can indicate disruption of the radar. The ECM can be selected from a library of countermeasures pre-verified as effective against known threats.

Realtime electronic countermeasure assessment

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.

Radar electronic countermeasures without a threat database

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.

Realtime electronic countermeasure optimization

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.

Artificial intelligence radio classifier and identifier

The discriminability of an RF fingerprint is increased by 'abstracting,' 'enhancing,' and &#x201c;reconstructing&#x201d; 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.

RF fingerprint enhancement by manipulation of an abstracted digital signal

An artifact-suppressing neural network (NN) kernel comprising at least one neural network, implemented in replacement of a DSP, provides comparable or better performance under non-edge conditions, and superior performance under edge conditions, due to the ease of updating the NN kernel training without enlarging its computational footprint or latency to address a new edge condition. In embodiments, the NN kernel can be implemented in a field programmable gate array (FPGA) or application specific integrated circuit (ASIC), which can be configured as a direct DSP replacement. In various embodiments, the NN kernel training can be updated in near real time when a new edge condition is encountered in the field. The NN kernel can include DCC lower layers and dense upper layers. Initial NN kernel training can require fewer examples. Example embodiments include a noise suppression NN kernel and a modem NN kernel.

Neural network kernels for signal processing in lieu of digital signal processing in radio receivers

A method of detecting electromagnetic signal sources of interest includes applying reinforcement learning to automatically and continuously update a receiver scan schedule wherein an agent is reinforced according to comparisons between expected and actual degrees of success after each schedule update, actual degrees of success being estimated by applying to signal data a plurality of value scales applicable to a plurality of reward classes. An exponential scale can be applied across the plurality of reward classes. A companion system can provide data analysis to the agent. The agent can include an actor module that determines schedule updates and a critic module that determines the degrees of scanning success and awards the reinforcements. Embodiments implement a plurality of agents according to asynchronous multiple-worker actor/critic reinforcement learning. The method can be initially applied to training data comprising synthetic and/or previously measured signal data for which the signal sources are fully characterized.

Autonomous reinforcement learning method of receiver scan schedule control

A method for predicting subject trustworthiness includes using at least one classifier to predict truthfulness of subject responses to prompts during a local or remote interview, based on subject responses and response times, as well as interviewer impressions and response times, and, in embodiments, also biometric measurements of the interviewer. Data from the subject interview is normalized and analyzed relative to an experience database previously created using data obtained from test subjects. Classifier prediction algorithms incorporate assumptions that subject response times are indicators of truthfulness, that subjects will tend to be consistently truthful or deceitful, and that conscious and subconscious impressions of the interviewer are predictive of subject trustworthiness. Data regarding interviewer impressions can be derived from interviewer response times, interviewer questionnaire answers, and/or interviewer biometric data. Appropriate actions based on trustworthiness predictions can include denial of security clearance or further investigation relevant to the subject.

Schema and method for deception detection

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.

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Framingham, MA, United States of America

Scott A Kuzdeba