Generalized cubature quadrature Kalman filters:derivations and extensions  

作　　者：Hongwei Wang Wei Zhang Junyi Zuo Heping Wang 

机构地区：[1]School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China [2]Experimental Aircraft Design and Flight Testing Laboratory of Shaanxi,Xi’an 710072,China [3]School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore 639798,Singapore

出　　处：《Journal of Systems Engineering and Electronics》2017年第3期556-562,共7页系统工程与电子技术（英文版）

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)

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.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.

关 键 词：cubature rule quadrature rule Kalman filter iterated method QR decomposition nonlinear estimation target tracking 

分 类 号：TN713[电子电信—电路与系统]