【摘要】 为了减少传统组合导航方法中舍去泰勒展开式的高次项对导航精度的影响,提出了一种基于扩展卡尔曼滤波的INS/WSN无偏紧组合方法。这种方法采用INS在本地相对坐标系的位置和速度误差作为系统变量,观测方程中未知节点理想的位置和速度被INS测量得到的位置和速度与其预估误差的差值替代。仿真结果显示,本文提出的方法平均位置误差比松组合方法降低20%左右,平均速度误差比松组合方法降低80%左右。更多还原

【Abstract】 In order to reduce the influence on the precision of traditional integrated navigation when ignoring the higher-order terms in Taylor expansion,an unbiased tightly-coupled integration of Inertial Navigation System(INS) with Wireless Sensor Network(WSN) based on Extended Kalman Filter(EKF) is proposed.This method employs position error and velocity error of INS in the relative coordinates as the system state variables.In the measurement equation,the ideal position values at unknown nodes are replaced by the INS-measured positions and their error estimations,and the ideal velocity values at unknown nodes are replaced by the INS-measured velocities and their error estimations.Simulation results show that the proposed approach has effectively reduced the mean error of the position by about 20% compared with loosely-coupled integration,and the mean error of the velocity has been decreased by about 80% compared with loosely coupled mode.更多还原