{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:18:33Z","timestamp":1760145513842,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Air Force Office of Scientific Research (AFOSR)","award":["FA9550-22-1-0364"],"award-info":[{"award-number":["FA9550-22-1-0364"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose a Bayesian Optimization (BO)-based strategy using the Gaussian Process (GP) for feature detection of a known but non-cooperative space object by a chaser with a monocular camera and a single-beam LIDAR in a close-proximity operation. Specifically, the objective of the proposed Space Object Chaser-Resident Assessment Feature Tracking (SOCRAFT) algorithm is to determine the camera directional angles so that the maximum number of features within the camera range is detected while the chaser moves in a predefined orbit around the target. For the chaser-object spatial incentive, rewards are assigned to the chaser states from a combined model with two components: feature detection score and sinusoidal reward. To calculate the sinusoidal reward, estimated feature locations are required, which are predicted by Gaussian Process models. Another Gaussian Process model provides the reward distribution, which is then used by the Bayesian Optimization to determine the camera directional angles. Simulations are conducted in both 2D and 3D domains. The results demonstrate that SOCRAFT can generally detect the maximum number of features within the limited camera range and field of view.<\/jats:p>","DOI":"10.3390\/s24154831","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T13:10:28Z","timestamp":1721913028000},"page":"4831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature Detection of Non-Cooperative and Rotating Space Objects through Bayesian Optimization"],"prefix":"10.3390","volume":"24","author":[{"given":"Rabiul Hasan","family":"Kabir","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Rutgers University, New Brunswick, NJ 08901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Rutgers University, New Brunswick, NJ 08901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"ref_1","unstructured":"Wilkins, M.P., Pfeffer, A., Schumacher, P.W., and Jah, M.K. 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