## 1 Introduction  
Optoelectronic platform has a wide range of applications in the field of aerospace, such as disaster prevention and rescue, geographic mapping, and precision agriculture [1, 2]. The line of sight (LOS) is the axis of the optoelectronic sensor lens. The stability of LOS of optoelectronic sensor can ensure the quality of the output image of optoelectronic platform. A high-quality feedback inertial signal is the basis of LOS motion control system in optoelectronic platform [3]. The processing of inertial sensor signal which affects the stabilization of LOS is an effective method. Gyro and accelerometer are two common inertial space motion sensors [4]. The LOS stability control of the optoelectronic platform needs the inertial velocity feedback of gyro. Gyro has a fast response but suffers from drift due to accumulated errors. In the application of the motion control method, even if the control command and feedback error are zero, the LOS of optoelectronic still shakes due to gyro drift [5]. Therefore, the velocity feedback of gyro measurement must be transformed into attitude feedback. In addition, maneuvering acceleration exists in the flight of aircraft. It adversely affects the output of the accelerometer and the angular attitude calculated from it [6]. Therefore, the attitude estimation based on multi-sensor data fusion method is necessary to improve the attitude control performance of optoelectronic platform. Many scholars do studies to solve the above problems. An attitude estimation algorithm based on gyro and accelerometer is proposed in reference [7]. However, the cumulative error of yaw attitude cannot be compensated for due to the lack of magnetometer information. An error data model of accelerometer and gyro is established and the attitude of moving carrier is predicted by Kalman filter [8; 9]. This method can suppress the attitude divergence caused by gyro drift but it is not applicable to the situation where the motion state changes rapidly. The system has poor robustness because the accelerometer measures the sum of motion acceleration and gravity acceleration when the carrier has maneuvering acceleration [10]. An extended Kalman filter (EKF) algorithm is applied to integrate accelerometer and magnetometer in [11]. During the maneuvering period, the estimation error of the maneuvering acceleration is modeled as the sum of a fluctuation error and a sudden change error. However, the magnetometer has measurement error due to electromagnetic interference which affects the measurement accuracy [12]. An improved adaptive Kalman filter (AKF) based on Sage-Husa time-varying state disturbance estimator is used to estimate the mean and variance of state disturbance, but this method is not stable and easy to diverge [13].The combination of KF and adaptive Kalman filter is also an effective data fusion method [14]. Reference [15] takes GPS/SINS integrated navigation as the background and adopts adaptive filter algorithm for attitude estimation. However, the measurement error of GPS is large when obstructions exist which affects the accuracy of the algorithm [16]. In addition, it needs to be processed synchronously with inertial sensor which increases the complexity of the algorithm. In order to solve the above problems, zero speed correction and speed constraint are applied to improve the accuracy and robustness of attitude estimation algorithm [17]. It means that the accurate detection of motion state of the moving carrier is very important. Reference [18] uses accelerometer variance and introduces yaw attitude for detection. The motion state is judged by the standard deviation (STD) of \(X\)-axis and \(Y\)-axis data of accelerometer and magnetometer [19]. These methods are mainly used in vehicle navigation system but there is little research on attitude measurement. In addition, it is difficult to meet the real-time and flexibility requirements of LOS stabilization control system of the optoelectronic platform. Furthermore, dynamic processes lead to phase lag.