Modification of Thermal Network Parameters for Aerial Cameras via Integrated Monte-Carlo and Least-Squares Methods

The precise thermal control of aerial cameras is crucial for the acquisition of high-resolution imagery, and an accurate temperature prediction is essential to achieve this. This paper presents a methodology for modifying thermal network models to improve the accuracy of temperature prediction for aerial cameras. Seven types of thermal parameters are extracted from the thermal network model, and a thermally sensitive analysis identifies eleven key parameters to streamline the processing time. Departing from traditional methods that rely on steady-state data, this study conducts transient thermal tests and leverages polynomial fitting to facilitate thorough parameter modification. To ensure data reliability, the Monte-Carlo algorithm is employed to explore the parameter spaces of key parameters, analyzing temperature errors. Subsequently, the Least-Squares method is utilized to obtain optimal estimates of the key parameter values. As a result, the updated model demonstrates significantly improved accuracy in temperature predictions, achieving a reduction in the maximum absolute error between the predicted and experimental results from 22 °C to 4 °C, and a lowering of the relative error from 33.8% to 6.1%. The proposed modification method validates its effectiveness in modeling and enhancing the precision of thermal network models for aerial cameras.