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首页> 外文会议>Saint Petersburg International Conference on Integrated Navigation Systems; 20040524-26; Saint Petersburg(RU) >ADAPTIVE ALGORITHM FOR POSITIONING DATA FUSION
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ADAPTIVE ALGORITHM FOR POSITIONING DATA FUSION

机译：定位数据融合的自适应算法

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The contribution presents continuous position determination algorithm for land navigation applications. The focus is on adaptive algorithm based on primary satellite navigation receiver and several additional positioning sensors with their error models adjusted by using special characteristics of a vehicle movement. The primary position measurement is affected by obstacles in urban areas, heavy foliage, etc. Under such conditions, the additional sensors (such as gyroscopes, accelerome-ters, and odometer) extrapolate change of movement from the last known position. The disadvantage of relative sensors is in their ageing accuracy that leads to poor results with increasing time. This behaviour results in an accumulation of sensor errors in projected trajectory. In the consequence of this accumulation, position error increases unboundedly with the time. Unfortunately sensors parameters are affected by movement dynamics. Well-suited method is then adaptive change of sensor's error model describing the actual vehicle situations. It is a learning process within intervals of precise measurements by primary navigation receiver in condition of dynamics and kinematics constraints, i.e. straight non-accelerated movement, well observed turn, etc. These characteristics obtained during learning process are then used as a correction of relative sensors error models. The purpose of the entire system is to provide the user with a precise position determination at any time.
机译：该贡献提出了用于陆地导航应用的连续位置确定算法。重点是基于主要卫星导航接收器和几个其他定位传感器的自适应算法，其误差模型可通过使用车辆运动的特殊特性进行调整。主要位置测量受到市区，大树等障碍物的影响。在这种情况下，其他传感器（例如陀螺仪，加速计和里程表）会根据最近的已知位置推断运动的变化。相对传感器的缺点在于其老化精度，随着时间的增加，结果会变差。此行为导致投影轨迹中传感器误差的累积。由于这种积累，位置误差随时间无限增加。不幸的是，传感器参数受运动动力学的影响。合适的方法是自适应地更改描述实际车辆状况的传感器误差模型。这是一个学习过程，它是在动态和运动学约束条件下，即由主导航接收器在精确测量间隔内进行的，即笔直的非加速运动，观察到的转弯等。然后，在学习过程中获得的这些特征将用作相对传感器的校正。错误模型。整个系统的目的是随时为用户提供精确的位置确定。

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