Minimum Error Entropy Based EKF for GPS Code Tracking Loop

作     者：Dah-Jing Jwo Jen-Hsien Lai 

作者机构：Department of CommunicationsNavigation and Control EngineeringTaiwan Ocean University202301TaiwanChina Chunghwa Precision Test Tech.Co.Ltd.Taoyuan City324TaiwanChina 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第6期

页      面：2883-2898页

核心收录：

学科分类：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 Ministry of Science and Technology Taiwan[Grant Numbers MOST 107-2221-E-019-025 and MOST 108-2221-E-019-013] 

主　　题：Entropy extended Kalman filter multipath global positioning system tracking loop 

摘      要：This paper investigates the minimum error entropy based extended Kalman filter(MEEKF)for multipath parameter estimation of the Global Positioning System(GPS).The extended Kalman filter(EKF)is designed to give a preliminary estimation of the *** scheme is designed by introducing an additional term,which is tuned according to the higher order moment of the estimation *** minimum error entropy criterion is introduced for updating the entropy of the innovation at each time *** to the stochastic information gradient method,an optimal filer gain matrix is *** mean square error criterion is limited to the assumption of linearity and ***,non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian *** of the existing multipath estimation algorithms are usually designed for Gaussian *** I(in-phase)and Q(quadrature)accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude,code delay,phase,and carrier *** reasonable way to obtain an optimal estimation is based on the minimum error entropy *** MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the *** assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.