Abstract: A system and method for forming a range estimate for a target with a laser detection and ranging system. The system includes a laser transmitter and an array detector. The method includes: transmitting a plurality of laser pulses; for each transmitted laser pulse: detecting, with the array detector, a plurality of ladar return photons from the laser pulse, each detection producing an electrical pulse; identifying, for each of the electrical pulses, a time bin of a plurality of time bins corresponding to the laser pulse, within which the electrical pulse was produced; forming a one dimensional range histogram array having, for each of a subset of the plurality of bins, an element with a value equal to the number of electrical pulses produced in the array detector during a time interval corresponding to the bin; and forming an estimated range rate for the target.

Abstract: A system and method for forming a range rate estimate for a target with a laser detection and ranging system including a laser transmitter and an array detector. The method includes: transmitting a plurality of laser pulses at a pulse repetition frequency; forming a one dimensional time series array corresponding to a time record of ladar return photons detected with the array detector; fitting the time series array with a superposition of a sine and a cosine of an initial value of a tentative frequency; iteratively fitting the time series array with a superposition of a sine and a cosine of the tentative frequency, and adjusting the tentative frequency until a completion criterion is satisfied at a final value of the tentative frequency.

Abstract: A system and method for forming an image of a target with a laser detection and ranging system. The system includes a laser transmitter and an array detector. The method includes transmitting a sequence of laser pulses; forming a plurality of point clouds, each point cloud corresponding to a respective transmitted laser pulse, each point in the point cloud corresponding to a point on a surface of the target; grouping the plurality of point clouds into a plurality of point cloud groups according to a contiguous subset of the sequence of laser pulses; forming a plurality of average point clouds, each of the average point clouds being the average of a respective group of the plurality of point cloud groups; and forming a first estimate of a six-dimensional velocity of the target, including three translational velocity components and three angular velocity components, from the plurality of average point clouds.

Abstract: A system and method for forming a range estimate for a target with a laser detection and ranging system. The system includes a laser transmitter and an array detector. The method includes: transmitting a plurality of laser pulses; for each transmitted laser pulse: detecting, with the array detector, a plurality of ladar return photons from the laser pulse, each detection producing an electrical pulse; identifying, for each of the electrical pulses, a time bin of a plurality of time bins corresponding to the laser pulse, within which the electrical pulse was produced; forming a one dimensional range histogram array having, for each of a subset of the plurality of bins, an element with a value equal to the number of electrical pulses produced in the array detector during a time interval corresponding to the bin; and forming an estimated range rate for the target.

Abstract: A system and method for forming an image of a target with a laser detection and ranging system. The system includes a laser transmitter and an array detector. The method includes transmitting a sequence of laser pulses; forming a plurality of point clouds, each point cloud corresponding to a respective transmitted laser pulse, each point in the point cloud corresponding to a point on a surface of the target; grouping the plurality of point clouds into a plurality of point cloud groups according to a contiguous subset of the sequence of laser pulses; forming a plurality of average point clouds, each of the average point clouds being the average of a respective group of the plurality of point cloud groups; and forming a first estimate of a six-dimensional velocity of the target, including three translational velocity components and three angular velocity components, from the plurality of average point clouds.

Abstract: A system and method for forming a range rate estimate for a target with a laser detection and ranging system including a laser transmitter and an array detector. The method includes: transmitting a plurality of laser pulses at a pulse repetition frequency; forming a one dimensional time series array corresponding to a time record of ladar return photons detected with the array detector; fitting the time series array with a superposition of a sine and a cosine of an initial value of a tentative frequency; iteratively fitting the time series array with a superposition of a sine and a cosine of the tentative frequency, and adjusting the tentative frequency until a completion criterion is satisfied at a final value of the tentative frequency.

Abstract: Embodiments of video and LIDAR target detection and tracking system and method for segmenting targets are generally described herein. In some embodiments, the system may perform moving target detection using three-frame difference processing on frames of LIDAR range images of a scene to identify a moving target and determine an initial position estimate for the moving target. The system may also perform segmentation of the moving target using a single frame of the LIDAR range images to generate a segmented target that may include a target mask and target centroid position. After segmentation, moving target registration including motion compensation may be performed by registering the segmented target in multiple LIDAR frames to determine a target position, a target orientation and a target heading. In some embodiments, a three-dimensional model of the moving target may be generated and added to a sequence of video frames.

Abstract: Embodiments of video and LIDAR target detection and tracking system and method for segmenting targets are generally described herein. In some embodiments, the system may perform moving target detection using three-frame difference processing on frames of LIDAR range images of a scene to identify a moving target and determine an initial position estimate for the moving target. The system may also perform segmentation of the moving target using a single frame of the LIDAR range images to generate a segmented target that may include a target mask and target centroid position. After segmentation, moving target registration including motion compensation may be performed by registering the segmented target in multiple LIDAR frames to determine a target position, a target orientation and a target heading. In some embodiments, a three-dimensional model of the moving target may be generated and added to a sequence of video frames.

Abstract: Embodiments of a target-tracking system and method of determining an initial target track in a high-clutter environment are generally described herein. The target-tracking system may register image information of first and second warped images with image information of a reference image. Pixels of the warped images may be offset based on the outputs of the registration to align each warped images with the reference image. A three-frame difference calculation may be performed on the offset images and the reference image to generate a three-frame difference output image. Clutter suppression may be performed on the three-frame difference image to generate a clutter-suppressed output image for use in target-track identification. The clutter suppression may include performing a gradient operation on a background image to remove any gradient objects.

Abstract: In one or more aspects of the present disclosure, a method, a computer program product and a system for reconstructing scene features of an object in 3D space using structure-from-motion feature-tracking includes acquiring a first camera frame at a first camera position; extracting image features from the first camera frame; initializing a first set of 3D points from the extracted image features; acquiring a second camera frame at a second camera position; predicting a second set of 3D points by converting their positions and variances to the second camera position; projecting the predicted 3D positions to an image plane of the second camera to obtain 2D predictions of the image features; measuring an innovation of the predicted 2D image features; and updating estimates of 3D points based on the measured innovation to reconstruct scene features of the object image in 3D space.

Abstract: Embodiments of a target-tracking system and method of determining an initial target track in a high-clutter environment are generally described herein. The target-tracking system may register image information of first and second warped images with image information of a reference image. Pixels of the warped images may be offset based on the outputs of the registration to align each warped images with the reference image. A three-frame difference calculation may be performed on the offset images and the reference image to generate a three-frame difference output image. Clutter suppression may be performed on the three-frame difference image to generate a clutter-suppressed output image for use in target-track identification. The clutter suppression may include performing a gradient operation on a background image to remove any gradient objects.

Abstract: In one or more aspects of the present disclosure, a method, a computer program product and a system for reconstructing scene features of an object in 3D space using structure-from-motion feature-tracking includes acquiring a first camera frame at a first camera position; extracting image features from the first camera frame; initializing a first set of 3D points from the extracted image features; acquiring a second camera frame at a second camera position; predicting a second set of 3D points by converting their positions and variances to the second camera position; projecting the predicted 3D positions to an image plane of the second camera to obtain 2D predictions of the image features; measuring an innovation of the predicted 2D image features; and updating estimates of 3D points based on the measured innovation to reconstruct scene features of the object image in 3D space.