Hrl Laboratories, LLC

The Boeing Company

Gm Global Technolgoy Operations, Inc.

The Aerospace Corporation

Hlr Laboratories

Peter Petre

Jose Cruz-Albrecht

Shankar R Rao

Zhiwei A Xu

Yen-Cheng Kuan

Albert E Cosand

Cynthia D Baringer

Timothy J Talty

James Chingwei Li

Mohiuddin Ahmed

Bryan H Fong

Narayan Srinivasa

Charles E Martin

Sarabjit Mehta

Joseph F Jensen

Gabriel L Virbila

Donald A Hitko

Peter S Wittenberg

Daniel Yap

Jonathan James Lynch

Gary Alan Ray

Andrew J MacDonald

John Pasiecznik, Jr

Roy M Matic

Sanaz Adl

Robert R Hayes

Aleksey Nogin

David Roderick Gerwe

Adour Vahe Kabakian

Youngkwan Cho

Brian N Limketkai

Vikas Kukshya

Alan E Reamon

Randall White

Daniel S Matic

Jason R Smith

Simon L Haxton

Joseph S Colburn

Willie W Ng

George C Valley

Emilio A Sovero

Todd S Kaplan

Shubha Kadambe

Susan L Morton

Hsuanyu Pan

Cathy (Xiangming) Kong

Perry Macdonald

Austin F Garrido

Mark W Wolboldt

Shubha Kadambo

Ining Ku

Willie N Ng

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP). The NeurACore CSP includes a NeurACore local learning layer block that is operable for receiving as an input a mixture of in-phase and quadrature (I/Q) signals and mapping the I/Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. A global learning layer is included that is operable for adapting the complex-valued output weights to predict a most likely next value of the input I/Q signal. Further, a neural combiner is included that operable for combining a set of delayed neural state vectors with weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

Complex neuromorphic adaptive core (neuracore) and physics enhanced neuromorphic adaptive controller

Techniques for denoising an electromagnetic signal are disclosed. The techniques utilize an antenna, a weight adaptation component, a reservoir computer including a computer interpretable neural network, a delay embedding component, and an output layer computer. The techniques include passively acquiring an electromagnetic signal by the antenna, producing a plurality of reservoir state values by the reservoir computer based on the electromagnetic signal, collecting the plurality of reservoir state values by the delay embedding component into a historical record, determining a plurality of reservoir state value weights by the weight adaptation component based at least in part of the historical record, scaling, by the plurality of reservoir state value weights, to produce a plurality of output values, the plurality of reservoir state values by the output layer computer, and outputting the plurality of output values, where the scaling occurs over a plurality of clock cycles of a clock for the system.

Pipelined cognitive signal processor

A method, apparatus, system, and computer program product for processing low frequency signals. A communications system comprising a low frequency receiver, a denoiser, and a signal extractor. The low frequency receiver receives low frequency signals in which a communications signal is expected. The denoiser is in communication with the low frequency receiver. The denoiser denoises the low frequency signals received from the low frequency receiver. The denoising results in a generation of denoised signals. The signal extractor in communication with the denoiser. The signal extractor extracts the communications signal from the denoised signal.

Very low frequency signals for underwater communications

Described is a system for Neuromorphic Adaptive Core (NeurACore) signal processor for ultra-wide instantaneous bandwidth denoising of a noisy signal. The NeurACore signal processor includes a digital signal pre-processing unit for performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time. The wideband complex valued I/Q input signal is decomposed into I and Q sub-channels. The NeurACore signal processor further includes a NeurACore and local learning layers for performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space; a global learning layer for performing a gradient descent online learning algorithm; and a neural combiner for combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.

Ultra-wide instantaneous bandwidth complex neuromorphic adaptive core processor

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP) for wide instantaneous bandwidth denoising of noisy signals. The NeurACore CSP includes a NeurACore block, a globally learning layer, and a neural combiner. The NeurACore block is operable for receiving as an input a mixture of in-phase and quadrature (I/Q) signals and mapping the I/Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. The global learning layer is operable for adapting the complex-valued output weights to predict a most likely next value of the input I/Q signal. Further, the neural combiner is operable for combining a set of delayed neural state vectors with the weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

Low size, weight and power (swap) efficient hardware implementation of a wide instantaneous bandwidth neuromorphic adaptive core (NeurACore)

Described is a cognitive signal processor that is implemented in a field programmable gate array (FPGA). During operation, the FGPA receives a continuous noisy signal. The continuous noisy signal is a time-series of data points from a mixture signal of waveforms having both noise and a desired waveform signal. The continuous noisy signal is linearly mapped to reservoir states of a dynamical reservoir. A high-dimensional state-space representation of the continuous noisy signal is generated by digitally combining the continuous noisy signal with the reservoir states. Notably, the continuous noisy signal is approximated over a time interval based on a linear basis function. One or more delay-embedded state signals are then generated based on the reservoir states. The continuous noisy signal is then denoised by removing the noise from the desired waveform signal, resulting in a denoised waveform signal.

Cognitive signal processor

A radar system includes a transmitter for transmitting a radio frequency (RF) signal or a radar signal and a plurality of receivers. Each receiver receives a plurality of reflected signals created by a plurality of targets reflecting the RF signal or radar signal. The reflected signals include background noise and each of the receivers are separated by a predetermined distance. The radar system also includes a multiple input de-noiser configured to de-noise input signals from the plurality of receivers and to determine a time difference of arrival of the reflected signals between the plurality of receivers. A detection and angular resolution module is configured to determine an angular resolution between the plurality of targets using the time difference of arrival of the reflected signals between the plurality of receivers.

Angular resolution of targets using separate radar receivers

A radar system including a transmit antenna for transmitting a radio frequency (RF) signal or a radar signal and a receive antenna for receiving a plurality of reflected signals created by a plurality of targets reflecting the RF signal or radar signal. The reflected signals include noise. The radar system also includes an analog-to-digital converter (ADC) that digitizes or samples the reflected signals to provide a digitized or sampled noisy input signal. The radar system further includes a reservoir computer that receives the noisy input signal. The reservoir computer includes a time-varying reservoir and is configured to de-noise the noisy input signal and provide a range measurement for each of the plurality of targets.

Below-noise after transmit (BAT) Chirp Radar

A method for removing an extracted RF signal to examine a spectrum of at least one other RF signal includes receiving a mixture signal by an ADC. The mixture signal includes a plurality of separate signals from different signal sources. The mixture signal is digitized by the ADC. A first digitized signal and a second digitized signal are generated that are the same. The first digitized signal is delayed a predetermined time delay and the second digitized signal is processed in a neuromorphic signal processor to extract an extracted signal. The predetermined time delay corresponds to a delay embedding in the neuromorphic signal processor. A phase delay and amplitude of the extracted signal is adjusted based on a phase delay and amplitude of the first digitized signal. An adjusted extracted signal is cancelled from the first digitized signal to provide an input examination signal for examination.

Signal removal to examine a spectrum of another signal

Described is a multi-input cognitive signal processor (CSP) for estimating time-difference-of-arrival (TDOA) of incoming signals. The multi-input CSP receives a mixture of input signals from an antenna a and an antenna b. The multi-input CSP predicts and temporally de-noises input signals a and b received from antennas a and b, respectively, using an input corresponding to each input signal, resulting in de-noised state vectors for input signals a and b. Using the de-noised state vectors for input signals a and b, cross-predicting and spatially de-noising the other of the de-noised state vectors for input signals a and b. TDOA values of signal pulses to each of antennas a and b are estimated and converted into estimated angles of arrival for each signal pulse.

Real-time time-difference-of-arrival (TDOA) estimation via multi-input cognitive signal processor

Described is a system for signal denoising using a cognitive signal processor having a time-varying reservoir. The system receives a noisy input signal of a time-series of data points from a mixture of waveform signals. The noisy input signal is linearly mapped into the time-varying reservoir. A high-dimensional state-space representation of the mixture of waveform signals is generated by combining the noisy input signal with a plurality of reservoir states. The system then generates a denoised signal corresponding to the noisy input signal.

Cognitive denoising of nonstationary signals using time varying reservoir computer

Described is a system for adaptive blind source separation. A time-series of data points from one or more mixtures of source signals is continuously passed through adaptable filters, where each filter has a corresponding output signal. An error of each output signal is determined, and a filter state of each filter is determined using the error signals. A set of filter center frequencies are adapted using the set of error signals and the filter states, resulting in new filter center frequencies. The set of filter center frequencies are updated with the new filter center frequencies. Finally, separated source signals are extracted from the mixture of signals.

Adaptive blind source separator for ultra-wide bandwidth signal tracking

A method for generating pulse descriptor words (PDWs) including frequency and/or bandwidth data from time-varying signals received by a sensor includes filtering, at a plurality of blind source separation (BSS) modules, signals derived from the time-varying signals, each BSS module including a filtering subsystem having a plurality of filter modules. Each filter module has a frequency filter coefficient (&#x3b1;) and is parameterized by a center frequency (f). The method also includes transmitting at least one blind source separated signal from the BSS modules to a PDW generation module communicatively coupled to the filtering subsystem. The method further includes generating, using the PDW generation module and based on the blind source separated signal, at least one PDW parameter vector signal containing the frequency data. The method also includes updating, upon generating and based on the PDW parameter vector signal, values of &#x3b1; and/or f for each filter module.

Systems and methods for pulse descriptor word generation using blind source separation

Described is a cognitive signal processor (CSP) for signal denoising. In operation, the CSP receives a noisy signal as a time-series of data points from a mixture of both noise and one or more desired waveform signals. The noisy signal is linearly mapped to reservoir states of a dynamical reservoir. A high-dimensional state-space representation is then generated of the noisy signal by combining the noisy signal with the reservoir states. A delay-embedded state signal is generated from the reservoir states. The reservoir states are denoised by removing noise from each reservoir state signal, resulting in a real-time denoised spectrogram of the noisy signal. A denoised waveform signal is generated combining the denoised reservoir states. Additionally, the signal denoising process is implemented in software or digital hardware by converting the state-space representation of the dynamical reservoir to a system of delay difference equations and then applying a linear basis approximation.

System for parallelized cognitive signal denoising

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Oak Park, CA, United States of America

Peter Petre