In free-space optical (FSO) communication, high-precision pointing is a critical technology required for rapid acquisition to reduce link establishment time and increase communication time. FSO communication on a motion platform is necessary to expand the communication area and to promote the establishment of a global communication network. However, the pointing accuracy of an optical communication terminal on a motion platform is low due to numerous error sources and error coupling. This paper evaluates the error sources and proposes a pointing model to avoid problems resulting from error coupling. This proposed pointing model was designed to improve the pointing accuracy of a gimbals-type optical communication terminal (GOCT) on a motion platform. The effectiveness of the proposed pointing model was verified by tracking star experiments. The modified residual error of the proposed point model was 94.8 μrad compared to 1324.2 μrad without correction. Additionally, the modified residual error was 94.8 μrad of the proposed pointing model compared to 140.2 μrad of the existing model. The actual open-loop pointing error was reduced from 150.4 μrad of the existing model to 101.3 μrad of the proposed model. Thus, the pointing accuracy of a GOCT on a motion platform was significantly improved after correction by the proposed pointing model.
High-precision pointing is a key technology in freespace optical communication, as it improves the acquisition probability and reduces the acquisition time. High-precision pointing of an optical communication terminal (OCT) on a motion platform is particularly important for establishing a global communication network. An OCT on a motion platform is subjected to both linear and nonlinear pointing errors. The present paper analyzes both types of pointing errors in detail. Linear pointing errors can be well corrected by the parameter model. This paper proposes a K-nearest neighbor (KNN) algorithm that corrects nonlinear pointing errors and verifies its effectiveness through tracking star experiments. After correcting the nonlinear pointing errors through KNN algorithm, the modified and actual open-loop pointing errors were 69.0 and 70.8 μrad, respectively, reduced from 87.3 and 91.9 μrad, respectively, in the parameter model. Thus, the proposed KNN algorithm can effectively improve the pointing accuracy of an OCT on a motion platform.
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