Generalized cubature quadrature Kalman filters:derivations and extensions

作     者：Hongwei Wang Wei Zhang Junyi Zuo Heping Wang 

作者机构：School of AeronauticsNorthwestern Polytechnical UniversityXi’an 710072China Experimental Aircraft Design and Flight Testing Laboratory of ShaanxiXi’an 710072China School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 639798Singapore 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2017年第28卷第3期

页      面：556-562页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(61473227 11472222) the Aerospace Technology Support Fund of China(2014-HT-XGD) the Natural Science Foundation of Shaanxi Province(2015JM6304) the Aeronautical Science Foundation of China(20151353018) 

主　　题：cubature rule quadrature rule Kalman filter iterated method QR decomposition nonlinear estimation target tracking 

摘      要：A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.