Abstract: Star tracker systems and methods are provided. The star tracker incorporates deep learning processes in combination with relatively low cost hardware components to provide moderate (e.g. ˜1 arc second attitude uncertainty) accuracy. The neural network implementing the deep learning processes can include a Hinton's capsule network or a coordinate convolution layer to maintain spatial relationships between features in images encompassing a plurality of features. The hardware components can be configured to collect a blurred or defocused image in which point sources of light appear as blurs, and in which the blurs create points of intersection. Alternatively or in addition, a blurred or defocused image can be created using processes implemented as part of application programming. The processing of collected images by a neural network to provide an attitude determination can include analyzing a plurality of blurs and blur intersections across an entire frame of image data.

Abstract: Multiple mode star tracker methods and systems in which attitude information and image information is generated are provided. The multiple mode star tracker includes a detector having a plurality of pixels arranged in a focal plane array. The detector is operated to obtain multiple image frames from within a field of view containing a plurality of stars. For each of the image frames, the attitude of the detector and in turn the attitude of each pixel is determined. Based on the attitude quaternion of the individual pixels within a plurality of frames, image data from the plurality of frames is co-added or stacked to form a composite image. The co-addition of multiple frames of image data enables or facilitates the detection of dim objects by the multiple mode star tracker.

Abstract: Multiple mode star tracker methods and systems in which attitude information and image information is generated are provided. The multiple mode star tracker includes a detector having a plurality of pixels arranged in a focal plane array. The detector is operated to obtain multiple image frames from within a field of view containing a plurality of stars. For each of the image frames, the attitude of the detector and in turn the attitude of each pixel is determined. Based on the attitude quaternion of the individual pixels within a plurality of frames, image data from the plurality of frames is co-added or stacked to form a composite image. The co-addition of multiple frames of image data enables or facilitates the detection of dim objects by the multiple mode star tracker.