基于联邦网络的传递对准滤波补偿算法  

Transfer Alignment Filtering Compensating Algorithm Based on Federal Neural Network

作　　者：赵剡[1] 王纪南[1] 解春明[1] 

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100191

出　　处：《北京理工大学学报》2012年第10期1077-1081,1090,共6页Transactions of Beijing Institute of Technology

基　　金：航空科学基金资助项目(20100818018)

摘　　要：针对空中环境各种干扰因素对空空导弹传递对准(TA)滤波的影响,提出一种基于联邦网络的补偿算法.将干扰误差考虑为量测输入纳入滤波系统,改进标准Kalman滤波结构.将补偿神经网络设计成联邦结构,两个子系统分别用于训练量测输入估计误差、输出层权值误差和隐层权值误差.推导了联邦网络的训练算法并对算法进行了稳定性证明,保证了网络在结构上计算量小,系统反馈能力强,能够对干扰误差进行有效在线预测,辅助改进Kalman滤波器对失准角进行精确估计.仿真比较实验验证了该算法能够在不需任何先验信息的条件下,及时适应对准环境,预测校正干扰误差,滤波收敛快、精度高,适合空空导弹在具体设备和环境条件下的快速精确传递对准.Focusing on the influence of various disturbing sources under air environment on transfer alignment （TA）, a compensating algorithm based on federal neural network was put forward. Firstly, standard Kalman filtering structure was improved by regarding disturbing errors as measurement input. Then, the neural network was designed to form federal structure with two subsystems, which were used to train measurement input estimating error, output layer weight error and hidden layer weight error. Further, the training algorithm of the federal neural network was deduced and its stability was proved, which ensured low computing load and strong feedback capability. The online disturbing errors were efficiently predicted and aided to modified Kalman filter for accurate estimation of misalignment. Results of simulation experiments validate that, without knowing any priori information, the proposed algorithm could timely adapt to TA environment, predict and rectify disturbing errors, achieve fast convergence and high accuracy. It is feasible for air-to-air missile to implement rapid and high accuracy TA under hard air environment with different apparatus.

关 键 词：空空导弹 传递对准 联邦网络 补偿 

分 类 号：U666.1[交通运输工程—船舶及航道工程]