基于粒子群优化的卡尔曼滤波去耦算法

针对相控阵雷达导引头由于前向通道增益和波束控制增益刻度尺度不同引起的扩展卡尔曼滤波(extended Kalman filter, EKF)去耦中误差量过大的问题,提出了基于粒子群优化的EKF去耦算法。采用了最小均方差为适应度函数,对两个增益参数进行组合优化,然后通过建立EKF的系统模型,推导了提取的视线角速率与增益参数之间的关系,使得滤波后的估计值为最优的后验估计。最后,通过仿真表明该算法可以很好地解决误差量过大的问题,并验证了所提算法在相控阵雷达导引头去耦和视线角速率提取中的有效性。

Kalman filtering decoupling algorithm based on particle swarm optimization

To solve the problem of large amounts of error in the extended Kalman filtering (EKF) decoupling algorithm caused by phased array radar seeker different control gain calibration scale of forward channel gain and beam control gain, an EKF decoupling algorithm based on the particle swarm optimization algorithm is proposed, which uses minimum mean square error as the fitness function. Two gain parameters are in optimum combination. Then through the establishment of the system model of EKF, the relationship between the line of sight rate and the gain parameters is deduced, so that the estimation after filtering is optimal posteriori estimation. Finally, the simulation result shows that the proposed algorithm is a good way to solve the problem of large amounts of error, and the algorithmic validity of decoupling and line of sight rate extraction in the phased array radar seeker is proved.