Abstract: A method includes receiving first and second images, each having a resolution that is less than or equal to a predetermined amount. The second image is captured at a different time than the first image, by a different sensor than the first image, or both. A common area is identified in both the first and second images. A probability of change in the common area between the first image and the second image is greater than a predetermined threshold. Third and fourth images are received, each having a resolution that is greater than or equal to the predetermined amount. The third and fourth images include the common area. The fourth image is captured at a different time than the third image, by a different sensor than the third image, or both. The fourth image has a total area that is less than a total area of the third image.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.