In order to improve the navigation accuracy of human occupied vehicle (HOV) precisely and efficiently, Researchers from Shenyang Institute of Automation (SIA), the Chinese Academy of Sciences (CAS) proposed an innovative hybrid approach based on unscented Kalman filter (UKF) and support vector machine (SVM) is to fuse integrated navigation data.

HOV is generally equipped with long baseline (LBL) acoustic positioning system and dead reckoning (DR) as an integrated navigation system. UKF is adopted to estimate the state of the dynamic model because of its good performance in filtering nonlinear problems. An accurate and stable filtering result can be obtained when both LBL and DR are online. At the same time, SVM is utilized to train DR information with the result when LBL outrages, and the particle swarm optimization (PSO) algorithm is employed for SVM parameters optimization.

Therefore, the integrated navigation system can maintain a good performance when the LBL is off-line. Simulation results with the real navigation data of Jiaolong HOV show that the methodology proposed here is able to meet the needs of HOV application.

Click the following link to see the original paper

http://jqr.sia.cn/CN/Y2015/V37/I5/614