The aim of the gimballed stabilization system is to stabilize the sensor's line of sight towards a target by isolating the sensor from the disturbance induced by the operating environment, such as various disturbance torques and body motions. This paper presents a two axes gimbal assembly. The torque relationships are derived considering the angular motion of the base body and the dynamic unbalance. The stabilization loops for the two axes gimbal system are constructed and related to each other with a cross-coupling unit. Next, the overall model is simulated using two approaches and the obtained results are compared to show the correction of the model proposed. Finally, numerous tests are applied to evaluate the model's performance and investigate the effects of torque disturbance considered in this research.
One the most important problems in target tracking are state estimation. This paper deals on estimation of states from noisy sensor measurements. Due to important of exact estimation in tracking problems must evader position and Line Of Sight angles estimated with least error rather than actual position. In this paper extended Kalman filter (EKF) and unscented Kalman filter (UKF) and Cubature Kalman Filter (CKF) are presented for bearing only Tracking problem in 3D using bearing and elevation measurements from tows sensors. The algorithms and model of system simulated using MATLAB and many tests were carried out. Simulation experiments show that the efficiency of EKF due to least RMSE have better performance on compared with the UKF algorithm. Also, the performance of EKF algorithm has been dramatically decreased when initialization (initial state assumption) is not good, which in this condition CKF method provides a more accurate approximation. Numerical results from Monte Carlo simulations show that the CKF have the best state estimation accuracy among all nonlinear filters considered. The proposed approach is interesting for the design of optimization algorithms that can run on target tracking systems.
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