title: Adaptive iterated extended Kalman filter and its application to autonomous integrated navigation for indoor robot.
abstract: As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, China ; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China.
authors.forename: Yuan
authors.identifier: 0000-0002-7452-4865
authors.initials: Y
authors.lastname: Xu
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, China ; Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing, China.
authors.forename: Xiyuan
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing, China ; School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, China.
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D009038:Motion; D012371:Robotics / Q000379:methods*; D018511:Systems Integration