Hrl Laboratories, LLC

The Boeing Company

Brian N Limketkai

Adour Vahe Kabakian

David W Payton

Qin Jiang

Soheil Kolouri

Daniel Yap

Peter Petre

Jeong-sun Moon

Shankar R Rao

Zhiwei A Xu

Tahir Hussain

Robert R Hayes

Kyung-Ah Son

Jongchan Kang

James H Schaffner

Keyvan R Sayyah

Guillermo A Herrera

Andrew C Keefe

Oleg M Efimov

Kang-Yu Ni

Pamela R Patterson

Willie W Ng

Raymond Sarkissian

Richard Michael Kremer

Erik S Daniel

Randall White

Joshua Haug

Willie N Ng

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 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 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

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

A continuous wave (CW) heterodyne light detection and ranging (LIDAR) air velocity sensor system that comprises a first light emitting structure arranged to send a signal light in a first direction in space; a second light emitting structure arranged to produce a local oscillator light having a wavelength different from the wavelength of the signal light by a predetermined wavelength; a receiver arranged to receive light from said first direction in space; and a first optical mixer for mixing the received light with said local oscillator light.

CW LIDAR wind velocity sensor for operation on a stratospheric vehicle

A neuroelectric sensor and stimulator system includes a first antenna, a reader coupled to the first antenna for transmitting stimulation controls and power to a second antenna, and for receiving sensor data transmitted from the second antenna via the first antenna, and at least one neuroelectric sensor stimulator array including the second antenna, a rectifier coupled to the second antenna for extracting power transmitted from the first antenna, a controller coupled to the second antenna for decoding controls transmitted from the first antenna to the second antenna for the neuroelectric sensor stimulator array, a plurality of sensors, a multiplexer coupled to the controller and to the plurality of sensors for selecting a single sensor, and a plurality of stimulators coupled to the controller for stimulating neurons, wherein the rectifier, the controller, the plurality of sensors, the multiplexer, and the plurality of stimulators include graphene.

Scalable high-density wireless neuroelectric sensor and stimulator array

A digital correlator including an input, a plurality of serially connected delay elements, wherein a first delay element of the plurality of serially connected delay elements is coupled to the input, a plurality of current elements, wherein each respective current element of the plurality of current elements is coupled to a respective delay element, and each current element has a current, and a summer for summing the currents of the plurality of current elements, the summer having an output for the digital correlator.

Digital correlator / FIR filter with tunable bit time using analog summation

The present invention describes a microresonator that can be used as a 1:f variable coupler in a unit cell. It is described how a cascade of unit cells can be used to form a tunable, higher-order RF-filter with reconfigurable passbands. The disclosed filter structure can be utilized for the narrowband channelization of RF signals that have been modulated onto optical carriers. It is also disclosed how to utilize add/drop capabilities of the contemplated microdisks to confer connectivity and cascading in two dimensions. The present invention can conveniently provide a wavelength division multiplexing router, where an array of unit cells as provided herein can form a programmable optical switching matrix, through electronic programming of filter parameters.

Reconfigurable optical filters formed by integration of electrically tunable microresonators

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Santa Monica, CA, United States of America

Brian N Limketkai