干扰条件下自适应滤波定位精度分析

Positioning accuracy analysis for adaptive Kalman filtering in measurements degradation

在干扰条件下,单纯采用自适应滤波(adaptive Kalman filter,AKF)或扩展卡尔曼滤波器(extensive Kalman filter,EKF)在全球导航卫星系统/惯性测量单元(global navigation satellite systems/inertial measurement units,GNSS/IMU)组合导航的运用中都无法达到系统精度最优。为了指导组合导航系统的数据融合滤波器设计,获取AKF和EKF定位性能的经验数值是十分必要的。首先推导出EKF和一种AKF算法——新息序列自适应估计(innovation-based adaptive estimation,IAE)的数学模型和计算公式。然后提出了一种实际数据结合仿真的验证方法。针对不同的干扰程度造成的精度降低的测量值,比较AKF算法跟普通EKF在GNSS/IMU组合导航数据融合中的定位精度性能。试验和仿真得到了在实验所采用的IMU精度条件下,自适应滤波在组合导航方面的定位性能的经验曲线以及IAE与EKF定位精度存在的临界点。

The global navigation satellite systems/inertial measurement units（GNSS/IMU） integrated system cannot achieve optimal performance with the coexistence of GNSS signal interface when adopting adaptive Kalman filter（AKF） or extensive Kalman filter（EKF） independtly.In order to design the data fusion filter of integrated navigation system,determining the the empirical value of the critical interference level for EKF and AKF is necessary.The essential equations of EKF and IAE are overviewed and a verification method is proposed.The method carries out field test to collect real data and imports simulated noise to the measurements to compare the positioning accuracy between innovation-based adaptive estimation（IAE）,which is one of the proved AKF algorithms,and EKF.The test and simulation determine the empirical value of the positioning performance and the critical interference level for IAE and EKF.