Indexed by:
Abstract:
Designed a zero velocity detection algorithm based on adaptive threshold in order to accurately detect the zero velocity moment, and through the kalman zero velocity correction algorithm timely correct the error of inertial navigation system(INS). Because at zero velocity correction time position and heading information is observed, by unscented kalman filter (UKF), INS and GPS are integrated navigation, and compared with extended kalman filter (EKF). Through the experiment under the condition of strong nonlinear, UKF has better filtering performance than EKF, and also better than the EKF on rapidity. Using kalman zero velocity correction and UKF building federal filtering model can effectively inhibit the INS position drift problem. At the same time, in straight line phase, the use of GPS location information correct course angle. When GPS signal loss within a certain time, the integrated navigation system can get the right course angle. Thus, the positioning accuracy of the integrated navigation system is obviously improved.
Keyword:
Reprint Author's Address:
Email:
Source :
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC)
ISSN: 1948-9439
Year: 2016
Page: 5237-5241
Language: English
Cited Count:
WoS CC Cited Count: 4
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 0
Affiliated Colleges: