Raytheon Company

Arthur M Newman

Brandon Crow

Vitaliy M Kaganovich

Joseph C Marron

Eran Marcus

Brian F Boland

Dave S Douglas

Nicholas Sun

Louis Zellinger

Mario Martinez

Pablo Arambel

A measured photo-event array is converted from two spatial coordinates and one temporal coordinate into three spatial coordinates for real-time imaging. Laser light pulses illuminate at least one object, and a Geiger-mode avalanche photodiode array receives photons from laser light reflected off the object. For each pulse of the laser light, the GMAPD outputs a first array of photo-events representative of reflected photons. A three-dimensional (3D) Gaussian distribution kernel arranged as a list of array locations to be processed and weight list(s) are provided. The weight list(s) specify an amount array values are scaled based on the Gaussian distribution or photon arrival time. A graphics processing unit arranges the first array of measured photo-events as a list, convolves the Gaussian displacement list with the list of measured photo-events to produce a convolution output, and applies weights from the weight list(s) to the values to produce a density point cloud.

Real-time image formation from Geiger-mode LADAR

A three-frame difference target-tracking system includes at least one processor configured to generate multiple aligned image frames of a scene. The aligned image frames include first, second, and third image frames. The at least one processor is also configured to identify differences between the first image frame and the second image frame, between the second image frame and the third image frame, and between the first image frame and the third image frame. The at least one processor is further configured to identify a direction of movement of a target in the scene based on the identified differences.

Three-frame difference target acquisition and tracking using overlapping target images

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.

Ladar range rate estimation using pulse frequency shift

Airborne tracking systems use sensors to track objects of interest. In order to track the objects of interests, the sensors need to be steered such that the object is kept, ideally, in the center of the sensors field of view. Automatic steering of optical sensors requires the generation of a track on an object of interest. When tracking boats on the water, current approaches to image processing may generate multiple detections on the object of interest. Embodiments of the present disclosure solve the track multiplicity problem by grouping tracks associated with the object of interest into a cluster and by estimating a most likely location of the object within the cluster of tracks. Based on the estimated location, embodiments of the present disclosure outputs a single track for the object. The single track is used by an automatic steering system to maintain a sensor aimed at the object of interest.

Method and system for processing a sequence of images to identify, track, and/or target an object on a body of water

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.

Three-frame difference moving target acquisition system and method for target track identification

Processes and systems for use in generating an approximation of an image from its edges project a respective scaled intensity value from each edge into regions abutting positive and negative sides of the edge. Positive and negative composite edge projection maps are generated, each including a respective combination of the projected scaled intensity values. A respective ratio of combined local intensity values is determined for each edge in each of the positive and negative composite maps. Respective intensity values weighted by the respective ratios are projected for each edge into regions abutting positive and negative sides of the edge. Revised positive and negative composite, weighted edge projection maps are generated, each including a respective combination of the projected, weighted intensity values.

Approximation of an imaged object from edges detected from the underlying image

Methods for detecting objects in an image. The method includes a) receiving magnitude and orientation values for each pixel in an image and b) assigning each pixel to one of a predetermined number of orientation bins based on the orientation value of each pixel. The method also includes c) determining, for a first pixel, a maximum of all the pixel magnitude values for each orientation bin in a predetermined region surrounding the first pixel. The method also includes d) summing the maximum pixel magnitude values for each of the orientation bins in the predetermined region surrounding the first pixel, e) assigning the sum to the first pixel and f) repeating steps c), d) and e) for all the pixels in the image.

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Sherman Oaks, CA, United States of America

Arthur M Newman