milemeter meaning in Chinese
里程计
Examples
- The thesis analyse the error and the feasibility of the system . because the precision of position and direction is related with the precision of the heading and the distance , the thesis discuss the method of obtaining the distance using milemeter , water milemeten engine ' s rotate speed and accelerometer , and the method of obtaining the heading using the combination of the gyrocompass and the digital compass . then the thesis introduce the details of the system realization , include the hardware and the software
由于航位推算的精度与航程和航向的精度直接相关，因此，论文讨论了用里程计、水速表、引擎转速或加速度计等获取里程的方法，用航向保持器和数字磁罗盘组合的方法获取航向角的方法(初始寻北由数字磁罗盘来完成) ，这为采用数据融合方法提高航程和航向精度打下了基础。 - Then this paper introduced the main method in multi - sensor integrated navigation - kalman filtering method , and a two - level optimization multi - sensor information fusion structure - combined filter which was originated by carlson and kerr , based on the structure of combined filter , it studied the method of navigating by the multi - sensor navigation system integrated by ins milemeter altimeter and piloting , then analyzed the effect of several filters . simulation proved that when altimeter were integrated , the height error was reduced a lot , and the combined filter is more effective than one - level kalman filter
然后，介绍了组合导航中的关键技术? ?卡尔曼滤波方法，以及一种二级最优多传感器融合结构? ? carlson , kerr等人提出的联合滤波器，并以联合滤波器的结构为基础研究了车载捷联惯导系统与里程计、气压高度计、地标组合导航的方法，比较了几种组合方法的效果。仿真结果表明，引入气压高度计可以有效的减小高度误差，二级联合滤波器的效果优于一级结构的卡尔曼滤波器。 - Because the ins error equation is unstable , some initial states error will cause error floating and error accumulating , if the filter observations were only position error , kalman filter will converge very slowly , and some states error ( such as yaw error ) will be great . since the milemeter altimeter and piloting could only output position information , this paper put forward a method , firstly estimateing states and then kalman filtering , to improve filtering effect . simulation proved that this method could effectively reduce the system states error , quicken filtering convergence and improve filtering precision
由于惯导系统( lsins )的误差方程是发散的，某些初始状态的误差会引起误差的漂移和积累，当观测量只有位置误差时，卡尔曼滤波的收敛速度很慢，某些状态(如方位角)误差很大，而以上除惯导外的其它导航传感器直接提供的只是位置信息，为了改善滤波器性能，本文根据里程计等传感器的特点，提出了首先对状态做出估计，然后在状态估计的基础上，进行卡尔曼滤波的方法。