###### Abstract
Since then, the Monte Carlo method [20] and its variations have been the preferred methods for correcting thermal models. [PERSON] et al. [21] accurately adjusted the thermal model of a thermally regulated satellite during ground testing through the effective implementation of the Monte Carlo hybrid algorithm. [PERSON] et al. [22] refined the sensitive parameters of a miniature satellite by employing a tiered approach with the Monte Carlo mixed method. Moreover, the Monte Carlo ray tracing (MCRT) method has emerged as a potent instrument for determining the solar direct incidence area and radiation transfer coefficient, thereby bolstering the accuracy of spacecraft thermal network models, as evidenced by previous studies [23; 24]. Recently, [PERSON] et al. [25] developed an innovative predictor-corrector Monte Carlo methodology, enabling a higher degree of precision in solving thermal radiative transfer equations. On the other hand, [PERSON] et al. [26] introduced a parameter self-correcting thermal network model, which dynamically adjusts the initial model parameters through real-time monitoring of IGBT solder layer aging. This approach effectively restores the accuracy of the thermal network model in calculating the junction temperature. In our previous work, a lumped-parameter thermal network (LPTN) model was established for an aerial camera, and thermal control design was performed based on the model prediction. However, temperature gradients of up to 3 \({}^{\circ}\)C were still observed in the optical system [27]. An integrated Monte Carlo and least squares method was used in a separate study [28] to modify the key thermal parameter values, but a discrepancy of up to 4 \({}^{\circ}\)remained between the calculated and measured temperatures of the lens after refinement of the thermal parameters. Later, to improve the computational accuracy of the thermal model, the genetic algorithm was used to search for global optimal parameter values to minimize the root mean square error (RMSE) between the calculated and measured temperatures to 1.07 \({}^{\circ}\)C [29]. In contrast to the previous work, the present study focuses on three key aspects: model structure modification, thermal parameter correction, and passive thermal control optimization design based on the thermal parameter sensitivity analysis. Parameter correction is achieved by using the Monte Carlo method as a parameter optimization algorithm. The aim is to investigate a different way to improve the accuracy of temperature projections for aerial cameras, thereby ensuring more accurate and reliable thermal control. The remainder of this paper is as follows: Section 2 introduces the prototype of the experimental aerial camera and the original thermal network model, including the key thermal parameters and their optimum value ranges identified by thermally sensitive analysis. Section 3 presents the passive thermal control optimization design, including the optimization of the frame insulation structure and the convective heat transfer coefficients, which ensures that the values of the key thermal parameters are kept within the optimum ranges. Section 4 outlines the procedure for correcting the thermal network model. Following an analysis of the consistency between the predicted temperatures and the transient thermal experimental data, the thermal network structure is modified. The Monte Carlo algorithm is used to explore thermal parameter spaces with varying numbers of samples in order to derive optimal parameter values. A comparative analysis is performed to contrast the results of the original model with those of the updated model. Finally, concluding remarks are presented in Section 5.