当多个传感器安装于同一载体之上,构成组合导航系统时,必然存在位置约束关系,当定位传感器等于或多于三个时,会产生多个这样的关系。利用这些条件,采用传统的约束滤波算法可以提高组合定位系统的整体精度,但有时也会造成高精度传感器的精度损失。本文中将这些位置约束条件看作观测量,通过设计适当的权矩阵,并结合自适应卡尔曼滤波算法,提出了一种用于提高传感器滤波精度的方法,并和传统的约束滤波算法进行了比较。仿真计算表明:在各传感器精度相当时,该算法可以提高各个传感器的精度,并和传统的约束滤波算法等效;当各传感器精度不同时,该算法仍然可以提高高精度传感器的滤波精度或保证高精度传感器的滤波精度不受损失。 When a plurality of sensors are mounted on the same carrier to form a combined navigation system, there must be a positional constraint relationship, and when the number of positioning sensors is equal to or more than three, a plurality of such relationships occur. Using these conditions, the traditional constraint filtering algorithm can improve the overall accuracy of the combined positioning system, but sometimes also lead to the loss of accuracy of high-precision sensors. In this paper, these position constraints are considered as observational quantities. By designing an appropriate weight matrix and combining with adaptive Kalman filter algorithm, a method for improving the sensor filtering accuracy is proposed, and compared with the traditional constrained filtering algorithm Compare The simulation results show that the algorithm can improve the accuracy of each sensor when the accuracy of each sensor is equivalent and is equivalent to the traditional constrained filtering algorithm. When the accuracy of each sensor is different, the algorithm can still improve the filtering accuracy or guarantee of the high-precision sensor High-precision sensor filtering accuracy without loss.