Constraints unscented particle filter and itsapplication in vehicle navigation(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2020年3期
- Page:
- 109-116
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Constraints unscented particle filter and itsapplication in vehicle navigation
- Author(s):
- ZHAO Yan; WANG Ning; YE Jikun
- (Air and Missile Defense College, Air Force Engineering University, Xian 710051, Shaanxi, China)
- Keywords:
- traffic engineering; constraints unscented particle filter; state estimation; vehicle navigation
- PACS:
- -
- DOI:
- -
- Abstract:
- A constraints unscented particle filter algorithm was proposed to deal with the low accuracy of vehicle integrated navigation, which was caused by interference and blockage of satellite navigation signal or longterm position error accumulation of dead reckoning in urban builtup area. Firstly, unscented Kalman filter was used to estimate the mean and variance of state in realtime. As the importance density function of particle sampling, Gaussian distribution overcomes the hard selection problem of importance function. Secondly, the method of constraint condition and constraint equation, which were constructed from observation equation and adopted to solve the defects of constraint conditions, which was hard to constructed and new constraint equation and leads to the computation surge. Then, Lagrange function was constructed to obtain the minimum value, which was projected to constraint plane by the state estimation. The vehicle kinetics constraint function and road constraint function were designed to constrain the state estimation and modify the big error estimation in order to improve the estimation accuracy of the state. Finally, the proposed algorithm was applied to simulation testing of GPS/DR integrated navigation system. The results show that compared with unscented particle filter and adaptive unscented particle filter, the precision of position error of proposed algorithm is controlled at about 1.5 m, while the precision of position error of other two algorithms are controlled at about 3 m. The estimated accuracy of position error of proposed algorithm performs better than other two algorithms and the positioning performance of integrated navigation is improved. Because of the improvement of the positioning performance of integrated navigation, reliable feedback information is provided and the occurrence of traffic accidents is avoided which in turn reduces casualties and economic losses. 1 tab, 9 figs, 25 refs.
Last Update: 2020-06-03