A method includes generating calibration data by geometrically calibrating first image data from a first camera unit relative to second image data from a second camera unit based on first descriptor data and second descriptor data. The first descriptor data is based on the first image data. The second descriptor data is based on the second image data. The calibration data is generated based on first position data corresponding to the first camera unit and second position data corresponding to the second camera unit. The method includes identifying, based on the calibration data, a target location relative to the first image data. The method further includes generating an output image that includes the first image data and an indication of where the target location is relative to a scene depicted in the first image data.

A firearm system includes a firearm and a computer. Electronics in the firearm determine data that includes a pathway between different points of aim of the firearm as the firearm moves. The computer receives this data and builds an image of the pathway between the different points of aim of the firearm.

An unmanned aerial vehicle (UAV) includes a propulsion system, a global navigation satellite system (GNSS) sensor, a camera and a controller. The controller includes logic that, in response to execution by the controller, causes the UAV to in response to detecting a loss of tracking by the GNSS sensor determine an estimated location of the UAV on a map based on a location image captured by the camera, determine a route to a destination using tracking parameters embedded in the map, wherein the map is divided into a plurality of sections and the tracking parameters indicate an ease of determining a location of the UAV using images captured by the camera with respect to each section, and control the propulsion system to cause the UAV to follow the route to the destination.

An augmented reality system includes a global navigation satellite system module adapted to output position data, an orientation measurement module adapted to output orientation data, an augmented reality module, at least one AR-client having a camera and a display. The augmented reality module is adapted to determine a position and orientation of the camera of the at least one AR-client based on the position data and orientation data, calculating screen positions of at least one AR object based on the position and orientation of the camera of the at least one AR-client to create at least one AR-overlay, transmitting the at least one AR overlay to at least one AR-client, and the AR-client is adapted to merging the at least one AR-overlay with a picture received from the camera of the at least one AR-client to provide an AR-image, and displaying the AR-image on the display.

A system and method provide improved remote turbulence measurement. The system includes an image capturing device that captures frames of images of a target. A controller of the system tracks motion of one or more features of the images between frames using a pattern recognition algorithm. The controller computes subpixel motion based on results of the pattern recognition algorithm and computes differential motion or tilts between pairs of features. The controller computes differential tilt variances from every set of frames. The controller computes theoretical weighting functions for differential tilt variances. The controller determines weights to linearly combine weighting functions such that the combined weighting function closely resembles a desired weighting function that corresponds to the turbulence parameter of interest. The controller linearly combines the differential tilt variances using the determined weights to obtain the turbulence parameter of interest.

Systems, methods, and computer-readable media are provided for an autonomous and fully automated method of validating multi-signature decoys that are configured to release infrared flares at multiple points after launch. In one aspect, a method includes capturing, using at least one image capturing device, raw image data of a launched decoy, the decoy having one or more segments configured to be released after launch and automatically processing the raw image data to (1) identify a release point for each of the one or more segments and (2) identify an infrared signature associated with each release point. The method further includes generating a visual display of the release point(s) of the one or more segments and the infrared signature(s) originating from the release point(s).

A scene-based non-uniformity correction method to achieve a fixed-pattern noise reduction and eliminate ghosting artifacts based on robust parameter updates via a confident inter-frame registration and spatio-temporally consistent correction coefficients. The method includes the steps of: Assessing an input image frame whether the input image frame has a sufficient scene detail for registrations to prevent false registrations originated from low-detail scenes, calculating horizontal and vertical translations between frames to find a shift, introducing a scene-adaptive registration quality metric to eliminate erroneous parameter updates resulting from unreliable registrations and applying a Gaussian mixture model (GMM)-based temporal consistency restriction to mask out unstable updates in non-uniformity correction parameters.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

Embodiments herein describe tracking the location and orientation of a target in a digital image. In an embodiment, this tracking can be used to control navigation for a vehicle. In an embodiment, a digital image can be captured by a visual sensor is received. A first array including a plurality of binary values related to the pixel velocity of a first plurality of pixels in the digital image as compared to corresponding pixels in a first one or more prior digital images can be generated. A second array including a plurality of values related to the standard deviation of pixel intensity of the first plurality of pixels in the digital image as compared to corresponding pixels in a second one or more prior digital images can be further generated. A plurality of thresholds relating to the values in the second array can be determined. A plurality of target pixels and a plurality of background pixels can be identified in the digital image, based on the first array, the second array, and the plurality of thresholds. A binary image related to the digital image, based on the identified plurality of target pixels and the identified plurality of background pixels, and identifying at least one of a location and an orientation of the target in the digital image based on the binary image, can be generated. In an embodiment, a command can be transmitted to a navigation system for a vehicle, to assist in navigating the vehicle toward the target, based on the identified at least one of a location and an orientation of the target.

A layered tactical information system and a method of sharing tactical information between multiple layers are provided. The method performed by a processor of each layer of the multiple layers includes searching for relevant data available to the processor of the layer in response to at least one of a layer request from the processor of another layer of the multiple layers and a directive received by a processor of a first layer of the multiple layers. The directive specifies an end state, one or more target types, a time window, a geographic location area, and a first layer requirement of at least one layer requirement to be resolved by the processor of the first layer. The method performed by the processor further includes evaluating whether a layer requirement of the at least one layer requirement for the layer is satisfied based on at least one of (a) any found relevant data that was found by the search and (b) layer data obtained from at least one of the other layers. When the layer requirement for the layer is not satisfied, the method performed by the processor further includes performing at least one of (a) transmitting, by the processor of the layer to the processor of another layer of the multiple layers, a layer request to gather further information and (b) generating layer data and providing the layer data generated to the processor of at least one other layer.