Abstract: 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.

Abstract: A computer system is disclosed for processing a sensor signal comprising a signal component and a noise component. A signal shifter is configured to generate a shifted version of the sensor signal, and a blind source separator (BSS) module is configured to process the sensor signal and the shifted version of the sensor signal to generate a signal output representing the signal component of the sensor signal.

Abstract: A method of mitigating jamming of a reflected energy ranging system for an autonomous vehicle is presented. The system comprises at least one transmission antenna, at least two receiving antennas, and a controller comprising a processor and a non-transitory computer-readable medium. The method comprises emitting an energy signal with the transmitter antenna, contacting a target with the energy signal, and reflecting the energy signal off the target and back towards the receiving antennas as a reflected energy signal. The method further comprises receiving a composite energy signal comprising at least the reflected energy signal and a jamming energy signal with the at least two receiving antennas, analyzing the composite energy signal with the processor to blindly extract at least the reflected energy signal and the jamming energy signal, and identifying which of at least the reflected energy signal and the jamming energy signal corresponds to the target with the processor.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

Abstract: A method of mitigating jamming of a reflected energy ranging system for an autonomous vehicle is presented. The system comprises at least one transmission antenna, at least two receiving antennas, and a controller comprising a processor and a non-transitory computer-readable medium. The method comprises emitting an energy signal with the transmitter antenna, contacting a target with the energy signal, and reflecting the energy signal off the target and back towards the receiving antennas as a reflected energy signal. The method further comprises receiving a composite energy signal comprising at least the reflected energy signal and a jamming energy signal with the at least two receiving antennas, analyzing the composite energy signal with the processor to blindly extract at least the reflected energy signal and the jamming energy signal, and identifying which of at least the reflected energy signal and the jamming energy signal corresponds to the target with the processor.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

Abstract: Described a system for anomaly detection using anomaly cueing. In operation, an input image having two-dimensional (2D) image mixtures of primary components is reformatted into one-dimensional (1D) input signals. Blind source signal separation is used to separate the 1D input signals into separate output primary components, which are 1D output signals. The 1D output signals are reformatted into 2D spatially independent component output images. The system then calculates all possible pair product images of the 2D spatially independent component output images and corresponding signal-to-noise ratios. A pair product image is selected based on the peak signal-to-noise ratio and thresholded to identify anomalies in the pair product image. Several types of devices can then be controlled based on the identified anomalies in the pair product image.

Abstract: A method for obstacle detection and navigation of a vehicle using resolution-adaptive fusion includes performing, by a processor, a resolution-adaptive fusion of at least a first three-dimensional (3D) point cloud and a second 3D point cloud to generate a fused, denoised, and resolution-optimized 3D point cloud that represents an environment associated with the vehicle. The first 3D point cloud is generated by a first-type 3D scanning sensor, and the second 3D point cloud is generated by a second-type 3D scanning sensor. The second-type 3D scanning sensor includes a different resolution in each of a plurality of different measurement dimensions relative to the first-type 3D scanning sensor. The method also includes detecting obstacles and navigating the vehicle using the fused, denoised, and resolution-optimized 3D point cloud.

Abstract: Described is a system for detection of network activities using transitive tensor analysis. The system divides a tensor into multiple subtensors, where the tensor represents communications on a communications network of streaming network data. Each subtensor is decomposed, separately and independently, into subtensor mode factors. Using transitive mode factor matching, orderings of the subtensor mode factors are determined. A set of subtensor factor coefficients is determined for the subtensor mode factors, and the subtensor factor coefficients are used to determine the relative weighting of the subtensor mode factors, and activity patterns represented by the subtensor mode factors are detected. Based on the detection, an alert of an anomaly is generated, indicating a in the communications network and a time of occurrence.

Abstract: Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

Abstract: A method is disclosed. The method receives a two dimensional or a three dimensional computer generated representation of an area, receives a plurality of images of the area captured by one or more video cameras, detects a first moving object in the plurality of images, generates a computer representation of the first moving object, correlates the images of the area captured by the one or more video cameras with the two dimensional or three dimensional computer generated representation of the area, and displays the computer representation of the first moving object in the two dimensional or the three dimensional computer generated representation of the area.

Abstract: A method for obstacle detection and navigation of a vehicle using resolution-adaptive fusion includes performing, by a processor, a resolution-adaptive fusion of at least a first three-dimensional (3D) point cloud and a second 3D point cloud to generate a fused, denoised, and resolution-optimized 3D point cloud that represents an environment associated with the vehicle. The first 3D point cloud is generated by a first-type 3D scanning sensor, and the second 3D point cloud is generated by a second-type 3D scanning sensor. The second-type 3D scanning sensor includes a different resolution in each of a plurality of different measurement dimensions relative to the first-type 3D scanning sensor. The method also includes detecting obstacles and navigating the vehicle using the fused, denoised, and resolution-optimized 3D point cloud.

Abstract: Described is a system for object recognition. The system generates a training image set of object images from multiple image classes. Using a training image set and annotated semantic attributes, a model is trained that maps visual features from known images to the annotated semantic attributes using joint sparse representations with respect to dictionaries of visual features and semantic attributes. The trained model is used for mapping visual features of an unseen input image to its semantic attributes. The unseen input image is classified as belonging to an image class, and a device is controlled based on the classification of the unseen input image.

Abstract: A method for increasing accuracy and reducing computational requirements for blind source separation of mixtures of signals in multi-path environments includes receiving a plurality of channel inputs, each channel input comprising a mixture of signals from a plurality of sources, performing a short time Fourier transform on each channel input of the plurality of channels, wherein a respective output of a respective short time Fourier transform on a respective channel is a respective time-frequency distribution for the respective channel, vectorizing each respective time-frequency distribution into a respective mixed frequency and time vector, combining each respective mixed frequency and time vector into a mixed frequency and time matrix, and performing blind source separation on the mixed frequency and time matrix to separate the mixture of signals from the plurality of sources into a plurality of signal source channels, each respective signal source channel comprising signals from a respective source.

Abstract: A method includes receiving image data at a first tracking system. The image data represents a region in an image of a sequence of images. The method also includes generating a first tracking fingerprint based on the image data. The method further includes comparing the first tracking fingerprint and a second tracking fingerprint. The method also includes providing an output from the first tracking system to a second tracking system based on a result of the comparison of the first tracking fingerprint and the second tracking fingerprint. The output includes an instruction associated with an object model stored at the second tracking system.

Abstract: Described is a system for detection of network activities using transitive tensor analysis. The system divides a tensor into multiple subtensors, where the tensor represents communications on a communications network of streaming network data. Each subtensor is decomposed, separately and independently, into subtensor mode factors. Using transitive mode factor matching, orderings of the subtensor mode factors are determined. A set of subtensor factor coefficients is determined for the subtensor mode factors, and the subtensor factor coefficients are used to determine the relative weighting of the subtensor mode factors, and activity patterns represented by the subtensor mode factors are detected. Based on the detection, an alert of an anomaly is generated, indicating a in the communications network and a time of occurrence.

Abstract: Described herein is a method of enhancing an image includes determining a level of environmental artifacts at a plurality of positions on an image frame of image data. The method also includes adjusting local area processing of the image frame, to generate an adjusted image frame of image data, based on the level of environmental artifacts at each position of the plurality of positions. The method includes displaying the adjusted image frame.

Abstract: Described is a system for determining crop residue fraction. The system includes a color video camera mounted on a mobile platform for generating a two-dimensional (2D) color video image of a scene in front or behind the mobile platform. In operation, the system separates the 2D color video image into three separate one-dimensional (1D) mixture signals for red, green, and blue channels. The three 1D mixture signals are then separated into pure 1D component signals using blind source separation. The 1D component signals are thresholded and converted to 2D binary, pixel-level abundance maps, which can then be integrated to allow the system to determine a total component fractional abundance of crop in the scene. Finally, the system can control a mobile platform, such as a harvesting machine, based on the total component fractional abundance of crop in the scene.