【摘要】 针对扩展卡尔曼滤波(EKF)在海底集矿车组合导航系统应用时存在着计算复杂、线性化误差大等问题,基于附加打滑参数的履带车运动学模型,将无色卡尔曼滤波(UKF)用于集矿车长基线声学导航(LBL)与推算导航(DR)的组合导航系统中.考虑到测量数据时延,组合导航系统融合LBL与DR信息,得到海底集矿车位置估计.研究结果表明:采用EKF方法,测量数据时延0,0.5,2s时,东向定位精度为0.14,0.32,0.48m,北向定位精度为0.13,0.28,0.44m;采用UKF方法,测量数据时延0,0.5,2s时,东向定位精度为0.10,0.26,0.37m,北向定位精度为0.09,0.24,0.34m.测量数据时延越短,EKF,UKF的位置估计效果都会越好.但与EKF方法相比,UKF方法能够明显减少组合导航系统的线性化误差,提高海底集矿车导航系统的精度与稳定性.更多还原

【Abstract】 Since there are some problems for an extended Kalman filter (EKF) to be deployed in an integrated deep-sea navigation system,including model errors caused by linearization and its complicated computation,the unscented Kalman filter (UKF) was introduced for the position estimation of a deep-sea mining vehicle based on a kinematic model in presence of sliding parameters. Taking into account the influence of measurement delays,the proposed navigation system fused both long-base line (LBL) data and the dead reckoning (DR) data to obtain the position estimates of the deep-sea mining vehicle. The results show that if EKF method is adopted,when measurement delay is 0,0.5,2 s respectively,the east position accuracy is 0.14,0.32,0.48 m respectively and north position accuracy is 0.13,0.28,0.44 m respectively. If UKF method is adopted,when measurement delay is 0,0.5,2 s respectively,the east position accuracy is 0.10,0.26,0.37 m respectively and north position accuracy is 0.09,0.24,0.34 m respectively. Both EKF and UKF have better localization accuracy if the measurement delay is shorter. The UKF can deal with nonlinear systems very well,and in particular has better accuracy and stability than EKF for the navigation system of a deep-sea mining vehicle.更多还原