Abstract: A system is disclosed that includes a detector and a combination of lenses and mirrors as imaging optics configured to provide for block by block compressive sensing of an imaged scene with the block by block determined by a plurality of shift-invariant masks to produce an image of the scene of the detector. A resolution of the image through the combination of lenses and mirrors is provided at the detector greater than a resolution capability of the detector at the detector. Another system and a method are also disclosed.

Abstract: Mechanisms for identifying energy received from a scene are provided. A coded aperture in an optical system receives energy from a scene. The coded aperture comprises a plurality of wavelength filter sets arranged in a predetermined pattern. Each wavelength filter set is configured to transmit energy in a corresponding wavelength band of a plurality of different wavelength bands. The coded aperture transmits the energy toward a detector array comprising a plurality of detector elements. The detector array generates sensor data that quantifies energy received by each detector element. The sensor data is processed based on the predetermined pattern to identify spatial locations of energy in each corresponding wavelength band with respect to the scene.

Abstract: A passive automatic target recognition (ATR) system includes a range map processor configured to generate range-to-pixel map data based on digital elevation map data and parameters of a passive image sensor. The passive image sensor is configured to passively acquire image data. The passive ATR system also includes a detection processor configured to identify a region of interest (ROI) in the passively acquired sensor image data based on the range-to-pixel map data, and an ATR processor configured to generate an ATR decision for the ROI.

Abstract: A passive automatic target recognition (ATR) system includes a range map processor configured to generate range-to-pixel map data based on digital elevation map data and parameters of a passive image sensor. The passive image sensor is configured to passively acquire image data. The passive ATR system also includes a detection processor configured to identify a region of interest (ROI) in the passively acquired sensor image data based on the range-to-pixel map data, and an ATR processor configured to generate an ATR decision for the ROI.

Abstract: A target detection system and method is disclosed that uses a trained detection component and an untrained detection component that enhances image data for candidate target detection. The trained detection component separates candidate targets from clutter within the image data using correlation filters trained from an image library. The image library includes target and clutter images that are used to tune the correlation filters. The untrained detection component separates the candidate targets from the clutter by suppressing clutter using a Fourier frequency transform of the image data. Anomalies are detected in the frequency domain, and retained to highlight candidate targets.

Abstract: A target detection system and method is disclosed that uses a trained detection component and an untrained detection component that enhances image data for candidate target detection. The trained detection component separates candidate targets from clutter within the image data using correlation filters trained from an image library. The image library includes target and clutter images that are used to tune the correlation filters. The untrained detection component separates the candidate targets from the clutter by suppressing clutter using a Fourier frequency transform of the image data. Anomalies are detected in the frequency domain, and retained to highlight candidate targets.

Abstract: A method and apparatus for detecting a pattern within an image. Image data (22) is received which is representative of the image. Filter values (70) are determined which substantially optimizes a first predetermined criterion (68). The first predetermined criterion (68) is based upon image data (22). A correlation output (40) is determined which is indicative of the presence of the pattern within the image data (22). The correlation output (40) is based upon the determined filter values (70) and the image data (22) via a non-linear polynomial relationship (78).

Abstract: A system and method for target reacquisition and aimpoint selection in missile trackers. At the start of iterations through the process, distance classifier correlation filters (DCCFs) memorize the target's signature on the first frame. This stored target signature is used in a subsequent confidence match test, so the current sub-frame target registration will be compared against the stored target registration from the first frame. If the result of the match test is true, a patch of image centered on the aimpoint is used to synthesize the sub-frame filter. A sub-frame patch (containing the target) of the present frame is selected to find the target in the next frame. A next frame search provides the location and characteristics of a peak in the next image, which indicates the target position. The DCCP shape matching processing registers the sub-frame to the lock coordinates in the next frame. This process will track most frames, and operation will repeat.