Hrl Laboratories, LLC

The Boeing Company

Url Laboratories, LLC

Adour Vahe Kabakian

David W Payton

Hung P Nguyen

Raviv Perahia

Logan D Sorenson

Shuoqin Wang

Brian N Limketkai

David T Chang

Qin Jiang

Soheil Kolouri

David L Walter

Geoffrey P McKnight

Peter Petre

Chia-Ming Chang

Amit Madanlal Patel

Bryan H Fong

Sanaz Adl

Yuri Owechko

Alan Jon Jacobsen

Tobias A Schaedler

William B Carter

David H Chow

Jeong-sun Moon

Shankar R Rao

David W Shahan

Keerti S Kona

Lian X Huang

Richard Michael Kremer

Gabriel L Virbila

John Jacob Ottusch

John L Visher

Austin F Garrido

Kangyu Ni

Joshua Haug

Hung Nguyen

Raviv Parahia

A method for synthetic aperture radar (SAR) phase history extraction includes receiving, at a SAR system, a set of SAR phase history data derived from a plurality of return signals, the plurality of return signals produced by the SAR system illuminating a scene with a plurality of radar pulses. A region of interest (ROI) is obtained, the ROI corresponding to a moving target within the scene. A doppler shift frequency range for the moving target is determined based at least in part on an azimuth angle spread corresponding to the ROI and a known approximate trajectory of the moving target. The SAR phase history data is filtered to give extracted phase history corresponding to the moving target based at least in part on the doppler shift frequency range.

Phase history extraction for moving target

Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.

System for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) system phase history

Disclosed is a synthetic aperture radar (SAR) system for target recognition with complex range profile. The SAR system comprising a memory, a recurrent neural network (RNN), a multi-layer linear network in signal communication the RNN, and a machine-readable medium on the memory. The machine-readable medium is configured to store instructions that, when executed by the RNN, cause the SAR system to perform various operations. The various operation comprise: receiving raw SAR data associated with observed views of a scene, wherein the raw SAR data comprises information captured via the SAR system; radio frequency (RF) preprocessing the received raw SAR data to produce a processed raw SAR data; converting the processed raw SAR data to a complex SAR range profile data; processing the complex SAR range profile data with the RNN having RNN states; and mapping the RNN states to a target class with the multi-layer linear network.

Complex recurrent neural network for Synthetic Aperture Radar (SAR) target recognition

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 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 spatial light modulator cell and arrays of spatial light modulator cells are disclosed. The spatial light modulator cells can comprise a phase change material (PCM) having a first side and a second side; an optical reflector configured to reflect an optical beam passing from the first side to the second side; and a PCM heater thermal conductively coupled to the PCM, wherein thermal modulation of the PCM modulates a phase of the PCM which varies light transmission through the PCM. Methods of making spatial light modulator cells and arrays are also disclosed.

Electrically switchable infrared mirrors using phase-change chalcogenides materials

An instrument package for use during the drilling a wellbore. The instrument package includes a plurality of instruments such as accelerometers, gyroscopes, and magnetometers; a computer is configured to determine the current position of the plurality of instruments from a set of measurements produced by the plurality of instruments; and wherein the plurality of instruments are mechanically isolated from a drill head assembly by one or more multi-degree of freedom vibration isolators. The computer preferably has at least two modes different analytical modes of analyzing the set of measurements produced by the plurality of instruments, including a continuous mode and a survey mode, the continuous mode being operational during times that active drilling is occurring and the survey mode being operational during times that the active drilling is not occurring.

Continuous trajectory calculation for directional drilling

A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

Target recognition from SAR data using range profiles and a long short-term memory (LSTM) network

Described is a system for adaptive calibration of a sensor of an inertial measurement unit. Following each sensor measurement, the system performs automatic calibration of a multi-axis sensor. A reliability of a current calibration is assessed. If the current calibration is reliable, then bias and scale factor values are updated according to the most recent sensor measurement, resulting in updated bias and scale factor values. If the current calibration is not reliable, then previous bias and scale factor values are used. The system causes automatic calibration of the multi-axis sensor using either the updated or previous bias and scale factor values.

Adaptive downhole inertial measurement unit calibration method and apparatus for autonomous wellbore drilling

A method for generating an output of a downhole inertial measurement unit (IMU) includes: generating a trajectory between a plurality of survey points of a planned well data as a function of time, wherein the planned well data includes a plurality of three-dimensional coordinates corresponding to the survey points of an underground planned well are used to generate a trajectory comprising a plurality of trajectory coordinates between the consecutive ones of the survey points; generating sensor data for each of the trajectory coordinates as a function of time based on the geodetic reference parameters, the generated sensor data comprising: generated accelerometer output; generated gyroscopic output; and generated magnetometer output; and outputting the generated accelerometer output; the generated gyroscopic output; and the generated magnetometer output as a function of time as a generated output of the downhole IMU.

System and method for generating output of a downhole inertial measurement unit

An acoustic absorber is disclosed. The acoustic absorber contains a plurality of adjacent passages defined by walls configured to generate alternating high and low pressure zones as an acoustic energy travels though the acoustic absorber.

Acoustic absorber

A method for providing control of surface waves propagating on a surface includes forming a surface treatment on the surface, wherein the surface treatment is configured to achieve a tensor surface admittance distribution matrix on the surface determined according to a modified transformation electromagnetics (tEM) equation.

Metasurfaces for improving co-site isolation for electronic warfare applications

Described is a system for estimating a trajectory of a borehole. The system processes signals of sensor streams obtained from an inertial sensor system. Using the set of processed signals, the system determines whether a drill is in a survey mode state or a continuous mode state, and a measured depth of the borehole is determined. A set of survey mode positioning algorithms is applied when the drill is stationary. A set of continuous mode navigation algorithms is applied when the drill is non-stationary. Using at least one Kalman filter, results of the set of survey mode positioning algorithms and the set of continuous mode navigation algorithms are combined. An estimate of a borehole trajectory and corresponding ellipse of uncertainty (EOU) is generated using the combined results.

Opportunistic sensor fusion algorithm for autonomous guidance while drilling

Several embodiments of systems and methods are described for a compound structure consisting of a compact conformal surface-wave antenna feed structure attached to a conformal surface-wave antenna. The feed structure is an Artificial Impedance Surface (AIS) which takes as input an arbitrary source, converts it into a desired surface-wave waveform, which then feeds its output into the attached conformal surface-wave antenna for optimal radiation performance. The feed structure can be made up of several sizes and shapes of AIS metal patches and can produce plane isotropic as well as anisotropic surface-wave output. The surface-wave antenna can be a radiating hologram made up of the same AIS metallic patches as the feed structure and fabricated on the same dielectric substrate.

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Monterey Park, CA, United States of America

Adour Vahe Kabakian