The Boeing Company

Mcdonnell Douglas Corporation

Ted L Johnson

Michael G Neff

Victor E Ratkowski, Jr

Steven A Wright

Shirley N Cheng

Chang-Shaung R Lee

Route search planner methods and systems are described. In an embodiment, a probability map can be generated from previous sensor scans combined with a projected target location of relocatable targets in a target area. A route can be generated by a route generator, based at least in part on the probability map, and based on optimal system performance capabilities utilized to search for at least one of the relocatable targets. A search manager can then assign an evaluation criteria value to the route based on route evaluation criteria, and compare the evaluation criteria value to other evaluation criteria values corresponding to respective previously generated routes to determine an optimal route. The search manager can then determine whether to generate one or more additional routes and assign additional evaluation criteria values for comparison to determine the optimal route.

Route search planner

Moving object detection methods and systems are described. In an embodiment, motion likelihoods are accumulated from sets of sequential image frames which are generated from a sensor scan of one or more moving objects. Regions that each indicates a probable moving object from the accumulated motion likelihoods are determined, and the one or more moving objects are then detected from the respective regions that indicate a probable moving object.

Moving object detection

Systems and methods having front end filters for data links for improved tracking of moving objects are disclosed. In one embodiment, a method includes sensing at least one characteristic of a moveable object using a sensor of an acquisition system, and sensing at least one characteristic of the moveable object using an auxiliary sensor. A data link update is transmitted from the auxiliary sensor to the acquisition system, and the data link update is conditioned to provide composite likelihood. The conditioning includes differencing the data link update and at least one predicted characteristic of the moveable object to produce at least one residual (e.g. using a plurality of Kalman filters). A viewing direction of the sensor may be adjusted based at least in part on the composite likelihood. In another embodiment, residuals are projected along sensor line of sight to form a multi-modal non-Gaussian composite likelihood.

Methods and systems for data link front end filters for sporadic updates

Methods and systems for detecting the insertion, removal, and change of objects of interest through the use of imagery are disclosed. In one embodiment, a method includes performing a scene registration including aligning image patterns in the first image to those in the second image; performing a feature content analysis to determine a likelihood of change for each pixel in the first and second images; performing a region identification to group pixels within the first and second images into one or more image regions based upon their likelihood of change; and performing an image region partitioning to prioritize the one or more image regions according to an image region score for each of the one or more image regions, the image region score being indicative of at least one of insertion, removal, and change of an object of interest within the common area of interest. Embodiments of the invention may advantageously provide an autonomous capability to detect the insertion, removal, and change of objects of interest.

Methods and systems for the detection of the insertion, removal, and change of objects within a scene through the use of imagery

Sensor scan planner methods and systems are described. In an embodiment, a sensor scan schedule can be generated by a sensor scan schedule generator to optimize the scan schedules of multiple sensors based on optimal capabilities of each sensor and autonomous target recognition algorithm processing. A search manager can then assign an evaluation criteria value to the generated sensor scan schedule based on sensor scan schedule evaluation criteria, and compare the evaluation criteria value to other evaluation criteria values corresponding to respective previously generated sensor scan schedules to determine an optimal sensor scan schedule. The search manager can then determine whether to generate additional sensor scan schedules and assign additional evaluation criteria values for comparison to determine the optimal sensor scan schedule.

Sensor scan planner

The image correlation method and apparatus correlates or matches a test image with a template. The image correlation apparatus includes an image processor for partitioning the template into a number of labels, for determining the total number of pixels N.sub.T which form the template and for determining the number of pixels N.sub.i which form each of the labels i. The image correlation apparatus also includes comparison means for comparing the test image to the template. The comparison means determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j,i representative of a predetermined gray level j which correspond to a predetermined label i of the template. The comparison means also determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j representative of a predetermined gray level j which correspond to the template. The image correlation apparatus further includes correlation means for determining the correlation X between the test image and the template according to a predetermined equation which is based, at least in part, upon N.sub.j,i, N.sub.j, N.sub.i and N.sub.T. The image correlation apparatus can also include an address generator for creating a number of relative offsets between the template and the test image. Thus, the test image can be compared to the template at each relative offset and the relative offset which provides the greatest correlation therebetween can be determined. Consequently, the test image and the template can be effectively matched such that a preselected object designated within the template can be located and identified within the test image.

Image processing method and apparatus for correlating a test image with

The image correlation method and apparatus correlates or matches a test image with a template. The image correlation apparatus includes an image processor for partitioning the template into a number of labels, for determining the total number of pixels N.sub.T which form the template and for determining the number of pixels N.sub.i which form each of the labels i. The image correlation apparatus also includes comparison means for comparing the test image to the template. The comparison means determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j,i representative of a predetermined gray level j which correspond to a predetermined label i of the template. The comparison means also determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j representative of a predetermined gray level j which correspond to the template. The image correlation apparatus further includes correlation means for determining the correlation X between the test image and the template according to a predetermined equation which is based, at least in part, upon N.sub.j,i, N.sub.j, N.sub.i and N.sub.T. The image correlation apparatus can also include an address generator for creating a number of relative offsets between the template and the test image.

The image correlation method and apparatus correlates or matches a test image with a template. The image correlation apparatus includes an image processor for partitioning the template into a number of labels, for determining the total number of pixels N.sub.T which form the template and for determining the number of pixels N.sub.i which form each of the labels i. The image correlation apparatus also includes comparator for comparing the test image to the template. The comparator respectively determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j,i representative of a predetermined gray level j which correspond to a predetermined label i of the template. The comparator respectively also determines, for each predetermined gray level j, the number of pixels of the test image N.sub.j representative of a predetermined gray level j which correspond to the template. The image correlation apparatus further includes correlation means for determining the correlation X between the test image and the template according to a predetermined equation which is based, at least in part, upon N.sub.j,i, N.sub.j, N.sub.i and N.sub.T. The image correlation apparatus can also include an address generator for creating a number of relative offsets between the template and the test image. Thus, the test image can be compared to the template at each relative offset and the relative offset which provides the greatest correlation therebetween can be determined. Consequently, the test image and the template can be effectively matched such that a preselected object designated within the template can be located and identified within the test image.

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Florissant, MO, United States of America

Ted L Johnson