For getting clear images and overcoming shaking caused by various disturbances, real-time compensation of pointing errors will improve the overall stability performance of photoelectric detection by unmanned aerial vehicle. However, the compensation will be greatly deteriorated by error-causing sources, and the error correction process is of great importance. In this research, the problem of stability precision error correction is comprehensively studied. First, by modeling overall kinematics, error-causing sources, and error compensation, the error correction process is mathematically modeled and simulated. Then, by using simulation data regression, error correction models including the global function model and parametric model are established. The models are validated by carrying out both simulations and validation experiments. At last, the performances of the error correction models are compared and analyzed, which concerns the factors of parameter identification, model simplicity, and final improvement effect. Results show that the final stability precision can be greatly improved over 20%, and the parametric model outperforms the global function model comprehensively. It can be concluded that, either in simulation environment or real application scenarios, the obtained models and related analysis results are effective in improving the system stability performance.

中文翻译：

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无人机光电检测的稳定性精度误差校正

为了获得清晰的图像并克服由于各种干扰而引起的抖动，对指向错误的实时补偿将提高无人机的光电检测的整体稳定性能。然而，由于产生错误的原因，补偿将大大恶化，并且错误校正过程非常重要。在这项研究中，对稳定性精度误差校正问题进行了综合研究。首先，通过对整体运动学，引起错误的源和错误补偿进行建模，对错误校正过程进行数学建模和仿真。然后，通过使用仿真数据回归，建立包括全局函数模型和参数模型的错误校正模型。通过执行仿真和验证实验来验证模型。终于，对纠错模型的性能进行了比较和分析，这些问题涉及参数识别，模型简化和最终改进效果的因素。结果表明，最终稳定性精度可以大大提高20％以上，并且参数模型在整体上优于全局函数模型。可以得出结论，无论是在仿真环境还是在实际应用场景中，所获得的模型和相关分析结果都可以有效地提高系统的稳定性。参数模型在整体上胜过全局函数模型。可以得出结论，无论是在仿真环境还是在实际应用场景中，所获得的模型和相关分析结果都可以有效地提高系统的稳定性。参数模型在整体上胜过全局函数模型。可以得出结论，无论是在仿真环境还是在实际应用场景中，所获得的模型和相关分析结果都可以有效地提高系统的稳定性。