Abstract: Methods and apparatus for providing a dynamic target impact point sweetener is disclosed. An example method includes identifying a target based on a composite three-dimensional image generated based on data received from a first aerial vehicle acting as a master vehicle and a second aerial vehicle acting as a slave vehicle; changing a role of the first aerial vehicle to the slave vehicle; changing the role of the second aerial vehicle to the master vehicle; and causing, using the second aerial vehicle acting as the master vehicle, a third vehicle to attack the target based on the identity of the target.

Abstract: Methods and apparatus for providing a dynamic target impact point sweetener is disclosed. An example method includes identifying a target based on a composite three-dimensional image generated based on data received from a first aerial vehicle acting as a master vehicle and a second aerial vehicle acting as a slave vehicle; changing a role of the first aerial vehicle to the slave vehicle; changing the role of the second aerial vehicle to the master vehicle; and causing, using the second aerial vehicle acting as the master vehicle, a third vehicle to attack the target based on the identity of the target.

Abstract: An apparatus for providing a dynamic target impact point sweetener may include memory and a processor. The memory may store at least a target library indicating respective target parameters for a plurality of known potential targets. The processor may be configured by stored instructions to generate a composite multi-dimensional representation of a target based on radar data received at the apparatus from other aerial vehicles collecting projections over an area in which the target is located and based on radar data collected by an aerial vehicle in which the apparatus is located, identify the target based on the composite multi-dimensional representation, and generate aimpoint data regarding the target based on an identity of the target. The aimpoint data defining the most vulnerable point on the target.

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: 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: In accordance with an embodiment, a system includes a plurality of vehicles and a central node. The plurality of vehicles each have radar systems used to collect radar data about the target object. Each in the plurality of vehicles moves in a path to collect a portion of the radar data using a sampling map to coordinate collection of the radar data by the plurality of vehicles and communicates with every other vehicle to identify uncollected portions of the radar data. The central node is in communication with the plurality of vehicles, wherein the central node receives the radar data from the plurality of vehicles and creates a three dimensional image from the radar data received from the plurality of vehicles using a tomographic reconstruction process.

Abstract: In accordance with an embodiment, a system includes a plurality of vehicles and a central node. The plurality of vehicles each have radar systems used to collect radar data about the target object. Each in the plurality of vehicles moves in a path to collect a portion of the radar data using a sampling map to coordinate collection of the radar data by the plurality of vehicles and communicates with every other vehicle to identify uncollected portions of the radar data. The central node is in communication with the plurality of vehicles, wherein the central node receives the radar data from the plurality of vehicles and creates a three dimensional image from the radar data received from the plurality of vehicles using a tomographic reconstruction process.

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: 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: 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.