一种基于渐消因子的区间卡尔曼滤波器方法  

Kalman Filtering for Ultra-Tight Coupled from Stable State to Catastrophe State in Noise Condition

作　　者：徐韩[1,2] 曾超[2] 黄清华[2] XU Han;ZENG Chao;HUANG Qinghua(School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621999, China)

机构地区：[1]南京理工大学电子工程与光电技术学院,江苏南京210094 [2]中国工程物理研究院电子工程研究所，四川绵阳621000

出　　处：《武汉大学学报（信息科学版）》2017年第12期1826-1833,1839,共9页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金委员会与中国工程物理研究院联合基金(U1330133)~~

摘　　要：在噪声环境中,运动目标发生稳态突变会降低卡尔曼滤波器的滤波性能,进而导致组合导航的可靠性降低,导航系统抗干扰能力下降,影响导航的精确度。为了提高卡尔曼滤波器性能,提高抗干扰能力和导航精度,在采用基于卡尔曼滤波器的超紧耦合同时,提出一种新型的基于渐消因子的区间卡尔曼滤波器算法。该算法通过引入渐消因子和区间矩阵对滤波器增益矩阵进行实时调整,并利用区间运算中的交集运算将各种误差源约束到交集区间,进而保证在区间运算中保真集合映射的完备性并取得最优化。结果显示,该算法能够克服原有滤波器算法的缺陷,在噪声环境中提升对稳态突变目标的跟踪能力,且在噪声中滤波器效果提高,算法计算量没有明显增加。In noise condition,high dynamic aerocraft change from stable state to catastrophe state will lead reduction of performance of Kalman filter,and then reduce reliability of GPS/INS integration,which influence anti-jamming of navigation system and reduce accuracy of navigation integration.In order to improve Kalman filter’s performance,enhance performance of anti-jamming of navigation system and increase accuracy of navigation integration,a new fading interval Quadrature Kalman filtering algorithm was proposed in this paper.The algorithm could adjust its filtering gain matrix on line and interval operation can not only ensure the completeness of set mapping but also can be the optimization by introducing a time-varying fading factor and interval matrix.The simulation performance statistics show that the new Kalman filtering algorithm can overcome limitation of QKF algorithm,which can improve performance of tracking.Complexity of algorithm was not increased acutely.

关 键 词：超紧耦合 锁相环 积分卡尔曼滤波器 稳态突变 区间运算 

分 类 号：P228.41[天文地球—大地测量学与测量工程]