Abstract
Unmanned Aerial Vehicles (UAVs) have significantly impacted various industries, with electro-optical (EO) pods playing a crucial role in enhancing their functionality. These pods require advanced stabilization algorithms to maintain a stable line of sight (LOS) in dynamic environments. This paper reviews the evolution of self-stabilization algorithms used in UAV EO pods, from traditional Proportional-Integral-Derivative (PID) controllers to advanced Kalman filters and machine learning techniques. Through a comprehensive analysis, the paper explores the application of these algorithms in different scenarios, highlighting their importance in both military and civilian domains. The findings provide insights into optimizing EO pod performance, ensuring high-quality imaging and precise targeting in various UAV operations.
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