基于CKF的联合扩维误差配准算法  

Augment State Registration Algorithm Based on CKF

作　　者：程然 何科峰 Cheng Ran;He Kefeng(AVIC Leihua Electronic Technology Research Institute, Wuxi 214063, China)

机构地区：[1]航空工业雷华电子技术研究所,江苏无锡214063

出　　处：《航空科学技术》2018年第5期66-73,共8页Aeronautical Science & Technology

基　　金：航空科学基金(2014ZC07003)~~

摘　　要：误差配准是多传感器信息融合的基础。为解决机载多平台多传感器的误差配准问题,研究并提出了一种基于容积卡尔曼滤波(CKF)的联合扩维误差配准算法。在算法实现中,首先采用状态矢量维数扩展方法建立非线性滤波框架下的系统误差配准模型,其次根据误差配准模型对各传感器的测量系统误差及各平台的姿态角系统误差进行估计,最后通过CKF滤波实现对状态预测值的修正,改善系统误差对滤波精度的影响。仿真结果表明,所提出的算法能够有效融合利用多传感器的测量信息,实现对多传感器系统误差及目标状态的实时联合精确估计。Sensor systematic error registration is the foundation of multi-sensor information fusion. In order to solve the problem of multi-sensor systematic error registration under the condition of multi-airborne platforms, an augment state registration algorithm based on Cubature Kalman Filter （CKF） was proposed. In the new algorithm, firstly the technique of state vector dimension expansion was adopted to establish the systematic error registration model in the nonlinear filtering framework. Secondly, the measurement systematic error of each sensor and the attitude systematic error of each platform were estimated effectively on the basis of systematic error registration model. Finally, the adverse effect of systematic error on filtering precision was reduced by the amendment of state prediction in CKF. Simulation results show that the algorithm can effectively fuse the multi-sensor measurement information, and achieve an exact and real-time joint estimation of multi-sensor systematic error and target state.

关 键 词：系统误差 误差配准 信息融合 容积卡尔曼滤波 

分 类 号：TN953[电子电信—信号与信息处理]