## 1 Introduction  
The CCD-based optoelectronic control system (OCS) is an important component in the high-precision capture and tracking optoelectronic platform, which is commonly used in astronomical observation, laser communication, target tracking and line-of-sight stabilization [1; 2; 3; 4]. With the expansion of generalization and commercialization, the OCS becomes smaller and is more installed on moving platforms, such as vehicles, airplanes, satellites, which are susceptible to external disturbance. Limited by the CCD's delay and low-sampling rate, the disturbance suppression(DS) performance of the outer position loop is extremely restricted [5; 6]. Generally, to enhance the DS, we attempted to use inertial sensors to build a high-sampling rate inner loop [7; 8]. As a member of the inertial sensors family, the MEMS accelerometer has advantages with low price, small size, high precision and high bandwidth and has been commonly used in telescopes, navigation and robots [9; 10; 11; 12]. However, the MEMS accelerometer also has a defect that in low frequency its signal is commonly polluted by noise and drift, leading to an insufficient DS in low frequency. Therefore, many researchers used a group of the accelerometer and gyro to guarantee a good DS [11; 13]. But more sensors mean more cost and space that is not suitable for the small and cost-efficient OCS. Previously, in the MEMS accelerometer and CCD based OCS, based on the acceleration and position dual-loop control(ADLC), Reference [14] combined an acceleration disturbance observer(DOB) with an additional virtual velocity loop to make some medium-frequency DS exchange for low-frequency performance, barely getting a better DS. Nevertheless, it is not optimal because the classic DOB based on accelerometers' inaccurate signals cannot observe accurate disturbance in low frequency and the velocity based on the time-domain fusion of a CCD and accelerometer carries the CCD's delay, resulting in a big drop of medium-frequency DS. To solve the problem, the most important is to get a more precise acceleration and velocity based on the existing sensors. As we know, when the OCS works to stabilize the line-of-sight, the CCD's data represent the platform's position signal. Although the CCD's signal is accompanied with imaging delay, its low-frequency component is still very good because the low-frequency signal's phase lag caused by the imaging delay is very small. On the contrary, the MEMS accelerometer with a high bandwidth has strength in high frequency while in low frequency its signal is weak and susceptible to drift and noise. Therefore, it is natural to combine the CCD's low-frequency signal with the MEMS accelerometer's relative high-frequency signal to get a better fusion velocity and acceleration. Sensors fusion has always been a hot topic. The common methods include the complementary filter way, the gradient descent algorithm and the Kalman way and so forth [15; 16; 17]. Gradient descent method is easy to fall into a local optimum. The Kalman filter as a time-domain filter needs to build a stable state space equation in which the sensors' noise and biases are often treated as a stochastic framework. And it has been successfully applied in some follow-up systems with high-sampling rate sensors free of delay [18; 19]. However, due to the CCD's non-negligible delay in this paper, the Kalman filter needs to predict the state of the future, which costs a lot of calculation and puts high demands on the hardware configuration. The complementary filter way proposed in this paper is mainly accomplished by a pair of complementary filters, which can respectively extract low-frequency and high-frequency signals. Compared with the other methods, the complementary filter based on frequency-domain fusion is easy to implement in engineering and can output a high-bandwidth motion state, without a large computational effort.