To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg–Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg–Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg–Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.

High line-of-sight (LOS) pointing precision is a prerequisite for improving the laser confrontation capability of a photoelectric interference pod. In a traditional photoelectric pod, the time delay in TV tracking reduces the system phase margin, system stability and LOS pointing precision. In view of this deficiency, a normalized LMS algorithm is introduced to compensate for the TV camera delay in the inner gimbal position loop of a two-axis and four-gimbal structure, which can allow a pod to avoid system phase margin reduction. Meanwhile, a fast steering mirror (FSM) system is used to improve the LOS pointing precision. First, this paper proposes a normalized LMS algorithm. Second, a compound control structure, with an outer gimbal analog controller and an inner gimbal laglead controller, is designed. Finally, the FSM beam control precision is analyzed. The experimental results show that the normalized LMS algorithm yields almost no delay; moreover, the azimuth and pitch beam control accuracies are greater by a factor of 15 and 3, respectively, compared with those of a conventional photoelectric pod.