Automatic detection of space objects in optical images is important to close proximity operations, relative navigation, and situational awareness. To better protect space assets, it is very important not only to know where a space object is, but also what the object is. In this dissertation, a method for detecting multiple 1U, 2U, 3U, and 6U CubeSats based on the faster region-based convolutional neural network (Faster R-CNN) is described. CubeSats detection models are developed using Web-searched and computer-aided design images. In addition, a two-step method is presented for detecting a rotating CubeSat in close proximity from a sequence of images without the use of intrinsic or external camera parameters. First, a Faster R-CNN trained on synthetic images of 1U, 2U, 3U, and 6U CubeSats locates the CubeSat in each image and assigns a weight to each CubeSat class. Then, these classification results are combined using Dempster's rule. The method is tested on simulated scenarios where the rotating 3U and 6U CubeSats are in unfavorable views or in dark environments. Faster R-CNN detection results contain useful information for tracking, navigation, pose estimation, and simultaneous localization and mapping. A coarse single-point attitude estimation method is proposed utilizing the centroids of the bounding boxes surrounding the CubeSats in the image. The centroids define the line-of-sight (LOS) vectors to the detected CubeSats in the camera frame, and the LOS vectors in the reference frame are assumed to be obtained from global positioning system (GPS). The three-axis attitude is determined from the vector observations by solving Wahba's problem. The attitude estimation concept is tested on simulated scenarios using Autodesk Maya.

Though the Aerospace industry is moving towards small satellites and smaller sensor technologies, sensors used for close-proximity operations are generally cost (and often size and power) prohibitive for University-class satellites. Given the need for low-cost, low-mass solutions for close-proximity relative navigation sensors, this research analyzed the expected errors due to near-field effects using a coarse sun sensor as part of a 6-degree-of-freedom (6-dof) solution. To characterize these near-field effects, a test bed (Characterization Test Apparatus or CTA) was proposed, its design presented, and the design stage uncertainty analysis of the CTA performed. A candidate coarse sun sensor (NorthStarTM) was chosen for testing, and a mathematical model of the sensor’s functionality was derived. Using a Gaussian Least Squares Differential Correction (GLSDC) algorithm, the model parameters were estimated and a comparison between simulated NorthStarTM measurements and model estimates was performed. Results indicate the CTA is capable of resolving the near-field errors. Additionally, this research found no apparent show stoppers for using coarse sun sensors for 6-dof solutions.