Hrl Laboratories, LLC

The Boeing Company

Shankar R Rao

Peter Petre

Kang-Yu Ni

Yuri Owechko

Charles E Martin

Soheil Kolouri

Bryan H Fong

Gabriel L Virbila

Narayan Srinivasa

Kyungnam Kim

Gary Alan Ray

Peter S Wittenberg

Brian N Limketkai

Andras Horvath

Daniel Yap

Swarup S Medasani

Jose Cruz-Albrecht

Mohiuddin Ahmed

Willie W Ng

Jiejun Xu

Adour Vahe Kabakian

Yongqiang Cao

Richard Michael Kremer

Matthew S Keegan

Daniel S Matic

Timothy J Derosier

Jason R Smith

Austin F Garrido

Simon L Haxton

Michael Cao

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

Described is a stripmap SAR system on a vehicle comprising an antenna that is fixed and directed outward from the side of the vehicle, a SAR sensor, a storage, and a computing device. The computing device comprises a memory, one or more processing units, and a machine-readable medium on the memory. The machine-readable medium stores instructions that, when executed by the one or more processing units, cause the stripmap SAR system to perform various operations. The operations comprise: receiving stripmap range profile data associated with observed views of a scene; transforming the received stripmap range profile data into partial circular range profile data; comparing the partial circular range profile data to a template range profile data of the scene; and estimating registration parameters associated with the partial circular range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Stripmap synthetic aperture radar (SAR) system utilizing direct matching and registration in range profile space

A system for real time bilinear interpolation includes a bilinear interpolation module capable of: generating pixel addresses for original image pixels of an original image needed for performing bilinear interpolation of the original image to form a resized image, wherein the generated pixel addresses assume all the original image pixels of the original image are accessible, and performing bilinear interpolation, and a pixel smart memory module capable: of sequentially receiving original image pixel rows of the original image an original image pixel row a time, predicting which original image pixel rows are needed for performing bilinear interpolation, storing only the needed sequentially received original image pixel rows in a memory, decoding the generated pixel addresses to form decoded addresses to access the needed original image pixel rows stored in the memory, and sending the needed original image pixel rows to the bilinear interpolation module for performing bilinear interpolation.

Efficient hardware design and implementation of image interpolation and resizing for image processing pipeline

A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Synthetic aperture radar (SAR) based convolutional navigation

Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.

Transport-based synthetic aperture radar navigation systems and methods

Systems and methods according to one or more embodiments are provided for mapping and registration of synthetic aperture raw radar data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory including executable instructions and a processor adapted to receive phase history data associated with observation views of a scene. The processor further converts the received phase history data associated with the observation views to a range profile of the scene. The range profile is compared to a range profile template of the scene to estimate a geometric transformation of the scene encoded in the received phase history data with respect to a reference template.

Synthetic aperture radar mapping and registration systems and methods

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 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

A method for improving computational efficiency for egomotion estimation includes detecting a pair of consecutive images, and estimating a rotation angle and a two dimensional translation vector that best models the motion between the image frames. The pair of images are each divided into overlapping image patches, and then an efficient projected correlation approach is used to estimate a local two dimensional translation vector for each image patch. The translation vectors from all the patches form a vector field, and then trimmed-mean method is used to robustly and efficiently estimate the rotation angle and the two dimensional translation that best models the egomotion of the detector, which may be a camera.

Efficient egomotion estimation using patch-based projected correlation

Described is a system that can recognize novel objects that the system has never before seen. The system uses a training image set to learn a model that maps visual features from known images to semantic attributes. The learned model is used to map visual features of an unseen input image to semantic attributes. The unseen input image is classified as belonging to an image class with a class label. A device is controlled based on the class label.

Zero shot machine vision system via joint sparse representations

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 video scene analysis system. The system includes a salience module that receives a video stream having one more pairs of frames (each frame having a background and a foreground) and detects salient regions in the video stream to generate salient motion estimates. The salient regions are regions that move differently than dominant motion in the pairs of video frames. A scene modeling module generates a sparse foreground model based on salient motion estimates from a plurality of consecutive frames. A foreground refinement module then generates a Task-Aware Foreground by refining the sparse foreground model based on task knowledge. The Task-Aware Foreground can then be used for further processing such as object detection, tracking or recognition.

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Agoura Hills, CA, United States of America

Shankar R Rao