Optoelectronic guidance and control
2015, 44(10): 3066-3069.
With obvious advantages comparing to gimbaled seekers, strap-down seekers attract more and more attention from military of different countries. Since strap-down seeker can not measure the line-of-sight(LOS) angular rate directly, the problem of LOS angular rate extraction is solved firstly, which generally has two methods. One method is to get the LOS angular rate by solving the differential of LOS angle versus time, the other is to estimate the LOS angular rate by Kalman filter. The models of these two methods was built and simulations of them were made respectively. The simulation result shows that, the second method is better than the first one. The time delay of LOS angular rate estimated by Kalman filter is relatively shorter than that obtained through differential network. With the measurement noises at the same level, the output LOS angular rate noise of the second method is smaller than the first one.
2015, 44(10): 3070-3075.
According to the characteristics of the line-of-sight kinematics in mirror stabilization platform, a general method based on the artificial mass stabilization platform was used to reconstruct LOS angle rate by utilizing the basic mirror kinematics equations. Then the relation between mirror rotation and the field-of-view was figured out. Due to the law of reflection, an auxiliary shaft must be made for mechanical mass stabilization or semi-strapdown stabilization. Analyzing this two methods deeply show that the methods are both feasible for mirror LOS stabilization control, but semi-strapdown stabilization would accord with the future needs.
2015, 44(10): 3076-3083.
Firstly, to solve the nonlinear problem in the field of passive tracking, Gauss-Hermite quadrature is used to Gaussian mixture probability hypothesis density filter, and the quadrature Kalman probability hypothesis density filter was proposed. Then under the condition of unknown and time-varying process noise statistic, a noise statistic estimator based on maximum a posterior estimation was used in probability hypothesis density filter. According to the residual between predicted state and estimated state, an algorithm to judge and restrain filter divergence was proposed. Finally, simulations under the condition that two passive sensors tracking multiple targets show that:the proposed algorithm has better accuracy than existing algorithms, and achieve good effect when process noise statistic is unknown and time-varying.
