论文部分内容阅读
针对机载IRST系统被动测距问题,给出了一种简化的UKF(SUKF)算法。算法无需对状态变量进行扩维,对于线性状态方程使用了和卡尔曼滤波相同的时间更新方法,对于非线性测量方程保持了UKF的特征,既继承了UKF的优点又减小了算法的运算量。仿真结果表明,SUKF在测距精度和收敛速度上明显优于EKF,其性能与标准UKF相同但计算耗时少,适合于机载IRST系统被动测距的实时处理。
Aiming at the passive ranging of airborne IRST system, a simplified UKF (SUKF) algorithm is presented. The algorithm does not need to expand the state variables. For the linear state equations, the same time update method as the Kalman filter is used. The UKF features are maintained for the nonlinear measurement equations, which not only inherit the advantages of the UKF but also reduce the computational complexity of the algorithm . The simulation results show that SUKF is superior to EKF in ranging accuracy and convergence speed. The performance of SUKF is the same as that of standard UKF, but its calculation time is short. It is suitable for real-time processing of passive ranging of airborne IRST system.