Abstract: A tracking system for tracking an expanded state of an object is provided. The tracking system comprises at least one processor and a memory having instructions stored thereon that, when executed by the at least one processor, cause the tracking system to execute a probabilistic filter that iteratively tracks a belief on the expanded state of the object, wherein the belief is predicted using a motion model of the object and is further updated using a compound measurement model of the object. The compound measurement model includes multiple probabilistic distributions constrained to lie on a contour of the object with a predetermined relative geometrical mapping to the center of the object. Further, the tracking system tracks the expanded state of the object based on the updated belief on the expanded state.

Abstract: An imaging system to reconstruct a reflectivity image of a scene including an object moving with the scene. A tracking system to track a deforming object to estimate an object deformation for each time step. Sensors acquire snapshots of the scene, each acquired snapshot of the object includes measurements in the object deformation for that time step, to produce a set of object measurements with deformed shapes over the time steps. Compute a correction to estimates of object deformation for each time step, with matching measurements of the corrected object deformation for each time step to measurements in the acquired snapshot of object for that time step. Select a corrected deformation over other corrected deformations for each time step, according to a distance between the corrected deformation and the estimate of the deformation, to obtain a final estimate of the deformation of the deformable object moving in the scene.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

Abstract: A radar system for generating a radar image of a scene. Receive radar measurements of reflectivity of each point in the scene measured by receivers. Solve a radar image recovery (RIR) problem using stored data to produce the radar image. By connecting the radar measurements to a shift of a reflection field with a receiver shift. The receiver shift defines an error between stored receiver positions and actual receivers positions, the reflection field is generated by reflecting the transmitted field from the scene in accordance with the reflectivity of each point in the scene. Connecting the reflection field to a shift of an incident field with a transmitter shift. The transmitter shift defines an error between stored transmitter positions and actual transmitters positions. Solve as a multilinear problem of joint estimation of the reflectivity of each point in the scene, the receiver shift, and the transmitter shift.

Abstract: An imaging system to reconstruct a reflectivity image of a scene including an object moving with the scene. A tracking system to track a deforming object to estimate an object deformation for each time step. Sensors acquire snapshots of the scene, each acquired snapshot of the object includes measurements in the object deformation for that time step, to produce a set of object measurements with deformed shapes over the time steps. Compute a correction to estimates of object deformation for each time step, with matching measurements of the corrected object deformation for each time step to measurements in the acquired snapshot of object for that time step. Select a corrected deformation over other corrected deformations for each time step, according to a distance between the corrected deformation and the estimate of the deformation, to obtain a final estimate of the deformation of the deformable object moving in the scene.

Abstract: A tomographic imaging system receives measurements at a set of frequencies of a wavefield scattered by an internal structure of an object, recursively reconstructs an image of the internal structure of the object until a termination condition is met, and renders the reconstructed image. For a current iteration, the system adds a frequency to a previous set of frequencies used during a previous iteration to produce a current set of frequencies, such that the added frequency is higher than any frequency in the previous set of frequencies, and reconstructs a current image of the internal structure of the object that minimizes a difference between a portion of the scattered wavefield measured at the current set of frequencies and a wavefield synthetized from the current image. A previous image determined during the previous iteration initializes the reconstruction of the current image.

Abstract: A radar image processing device is provided for generating a radar image from a region of interest (ROI). The radar image processing device receives transmitted radar pulses and radar echoes reflected from the ROI at different positions along a path of a moving radar platform and stores computer-executable programs including a range compressor, a graph modeling generator, a signal aligner, a radar imaging generator and a focused image generator.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

Abstract: A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.

Abstract: A system for detecting faults of an electric machine is provided. The system includes an interface, a memory to store computer-implemented programs including a signal sampling program, a matrix formation program, an optimization formation program, a matrix pencil program, optimization solvers and lookup data including predetermined system parameters related to the faults, and a processer.

Abstract: A system and a method for tracking an expanded state of an object including a kinematic state indicative of a position of the object and an extended state indicative of one or combination of a dimension and an orientation of the object is provided herein. The system comprises at least one sensor configured to probe a scene including a moving object with one or multiple signal transmissions to produce one or multiple measurements of the object per the transmission, and a processor configured to execute a probabilistic filter tracking a joint probability of the expanded state of the object estimated by a motion model of the object and a measurement model of the object, wherein the measurement model includes a center-truncated distribution having predetermined truncation intervals. The system further comprises an output interface configured to output the expanded state of the object.

Abstract: A system and a method for tracking an expanded state of an object including a kinematic state indicative of a position of the object and an extended state indicative of one or combination of a dimension and an orientation of the object is provided herein. The system comprises at least one sensor configured to probe a scene including a moving object with one or multiple signal transmissions to produce one or multiple measurements of the object per the transmission, and a processor configured to execute a probabilistic filter tracking a joint probability of the expanded state of the object estimated by a motion model of the object and a measurement model of the object, wherein the measurement model includes a center-truncated distribution having truncation intervals. The system further comprises an output interface configured to output the expanded state of the object.

Abstract: A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.

Abstract: An image processing system for determining an image of a scene using AI modules that are agnostic to image acquisition settings. The system comprises a processor that executes an image acquisition settings estimator module to estimate a forward operator using setting data. Further, recursively estimates the image of the scene until a termination condition is met, where for a current iteration, the processor executes an image acquisition inverter module with the estimated forward operator and a previous estimate of the image of the scene estimated during a previous iteration to produce the current noisy image, executes a noise level estimator module with the previous estimate of the image to produce a current noise level, and executes a denoiser to remove the current noise level from the current noisy image to produce a current image.

Abstract: An image processing system for determining an image of a scene using AI modules that are agnostic to image acquisition settings. The system comprises a processor that executes an image acquisition settings estimator module to estimate a forward operator using setting data. Further, recursively estimates the image of the scene until a termination condition is met, where for a current iteration, the processor executes an image acquisition inverter module with the estimated forward operator and a previous estimate of the image of the scene estimated during a previous iteration to produce the current noisy image, executes a noise level estimator module with the previous estimate of the image to produce a current noise level, and executes a denoiser to remove the current noise level from the current noisy image to produce a current image.

Abstract: A radar system for generating a radar image of a scene. Receive radar measurements of reflectivity of each point in the scene measured by receivers. Solve a radar image recovery (RIR) problem using stored data to produce the radar image. By connecting the radar measurements to a shift of a reflection field with a receiver shift. The receiver shift defines an error between stored receiver positions and actual receivers positions, the reflection field is generated by reflecting the transmitted field from the scene in accordance with the reflectivity of each point in the scene. Connecting the reflection field to a shift of an incident field with a transmitter shift. The transmitter shift defines an error between stored transmitter positions and actual transmitters positions. Solve as a multilinear problem of joint estimation of the reflectivity of each point in the scene, the receiver shift, and the transmitter shift.

Abstract: Systems and methods for multiple image registration of images of a scene or an object. Receiving image data, the image data includes images collected from different measurements of a single modality or multiple modalities, either at different rotation angles, horizontal shifts, or vertical shifts, of the scene or the object. Estimating registration parameters, using pairs of images, each pair of images includes a reference image and a floating image. Generating parameter matrices corresponding to registration parameters using an image registration process for all pairs of images. Decomposing each parameter matrix into a low-rank matrix of updated registration parameters and a sparse matrix corresponding to the registration parameter errors for each low-rank matrix, to obtain updated registration parameters for robust registration. Using the updated registration parameters to form a transformation matrix to register the images with at least one reference image, resulting in robust registration of the images.

Abstract: Systems, methods and apparatus for image processing for reconstructing a super resolution (SR) image from multispectral (MS) images. A processor to iteratively, fuse a MS image with an associated PAN image of the scene. Each iteration includes using a gradient descent (GD) approach with a learned forward operator, to generate an intermediate high-resolution multispectral (IHRMS) image with an increased spatial resolution and a smaller error to the DSRMS image compared to the stored MS image. Project the IHRMS image using a trained convolutional neural network (CNN) to obtain an estimated synthesized high-resolution multispectral (ESHRMS) image, for a first iteration. Use the ESHRMS image and the PAN image, as an input to the GD approach for following iterations. The updated IHRMS image is an input to another trained CNN for the following iterations. After predetermined number of iterations, output the fused high-spatial and high-spectral resolution MS image.

Abstract: Systems and methods for multiple image registration of images of a scene or an object. Receiving image data, the image data includes images collected from different measurements of a single modality or multiple modalities, either at different rotation angles, horizontal shifts, or vertical shifts, of the scene or the object. Estimating registration parameters, using pairs of images, each pair of images includes a reference image and a floating image. Generating parameter matrices corresponding to registration parameters using an image registration process for all pairs of images. Decomposing each parameter matrix into a low-rank matrix of updated registration parameters and a sparse matrix corresponding to the registration parameter errors for each low-rank matrix, to obtain updated registration parameters for robust registration. Using the updated registration parameters to form a transformation matrix to register the images with at least one reference image, resulting in robust registration of the images.

Abstract: Systems and methods for radar systems to produce a radar image of a region of interest (ROI with targets. Sensors to transmit source signals to the ROI and to measure echoes reflected back from the targets corresponding to the transmitted source signals. A processor to calculate an estimate of a noisy and a partial Euclidean Distance Matrix (EDM) of the sensors and the targets. Decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process. The low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations. Implement an inverse imaging process using the estimated actual sensor locations and the received data, to produce the radar image to output to a communication channel.