Title :
Improved particle filter algorithm for INS/GPS integrated navigation system
Author :
Sun, Feng ; Tang, Lijun
Author_Institution :
Autom. Coll., Harbin Eng. Univ., Harbin, China
Abstract :
The INS/GPS navigation system is obvious nonlinear under the large initial condition errors. To tackle the accuracy of integrated navigation under nonlinear model, a improved particle filter named cubature particle filter (CPF) is applied to INS/GPS integrated navigation. For this, the nonlinear state model based on the platform misalignment angle and the observation model described by the velocity error and position error is established. The CPF that utilizes the current observation information in the prior updating phase develops the importance proposal distribution function by cubature kalman filter (CKF) to relieve particle sample degeneracy and impoverishment, so the improved particle algorithm significantly improves the accuracy of the integrated navigation system than traditional particle filter (PF).
Keywords :
Global Positioning System; Kalman filters; nonlinear filters; particle filtering (numerical methods); CPF; INS-GPS integrated navigation system; condition error; cubature Kalman filter; cubature particle filter; distribution function; nonlinear state model; particle algorithm; particle filter algorithm; particle sample degeneracy; platform misalignment angle; position error; velocity error; Estimation; Global Positioning System; Kalman filters; Mathematical model; Particle filters; Proposals; CKF; CPF; INS/GPS; importance proposal distribution; particle filter;
Conference_Titel :
Mechatronics and Automation (ICMA), 2011 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8113-2
DOI :
10.1109/ICMA.2011.5986361