Application of modified SRCKF algorithm in MEMS inertial navigation system

CAI Xiaomo; ZHANG Wei; GUO Zilong

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Application of modified SRCKF algorithm in MEMS inertial navigation system

Applications | 更新时间：2025-12-30

- Application of modified SRCKF algorithm in MEMS inertial navigation system
- Vol. 44, Issue 11, Pages: 164-168(2025)
- 作者机构：
  
  1. 北京信息科技大学传感器重点实验室,北京,100101
  2. 西安工业大学光电工程学院,陕西,西安,710021
- 作者简介：
- 基金信息：
- DOI：
  CLC： U666;TP212
- Published：2025
- 稿件说明：
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CAI Xiaomo, ZHANG Wei, GUO Zilong. Application of modified SRCKF algorithm in MEMS inertial navigation system[J]. 2025, 44(11): 164-168.
DOI：

CAI Xiaomo, ZHANG Wei, GUO Zilong. Application of modified SRCKF algorithm in MEMS inertial navigation system[J]. 2025, 44(11): 164-168. DOI：

摘要

为了提高MEMS惯性导航在姿态解算方面的精度，提出了一种改进平方根容积卡尔曼滤波（MSRCKF）算法。该算法将四元数和陀螺漂移作为系统状态，将加速度计数据作为系统观测量。以平方根容积卡尔曼滤波（SRCKF）为基础，利用残差向量构造抗差加权矩阵实时调节量测噪声矩阵，减小异常观测值对姿态解算的影响，以提高系统的抗干扰能力。设计不同的实验场景，并与扩展卡尔曼滤波（EKF）、容积卡尔曼滤波（CKF）进行对比。结果表明：在静态实验中，利用MSRCKF方法测得的姿态均方根误差小于0.3°，动态实验中均方根误差小于0.5°。该方法相比于EKF

CKF，能明显提高姿态解算的精度。

Abstract

In order to improve the precision of MEMS inertial navigation in attitude solving

a modified square root cubature Kalman filtering(MSRCKF)algorithm is proposed.The algorithm takes the quaternion and gyro drift as the system states and the accelerometer data as the system observations.Based on the square root cubature Kalman filtering (SRCKF)

the residual vector is used to construct the robust weighting matrix to adjust the measurement noise matrix in real time

and reduce the influence of the abnormal observations on the attitude solving

in order to improve the anti-jamming capability of the system.Different experimental scenarios are designed and compared with extended Kalman filtering(EKF) and cubature Kalman filtering(CKF).The results show that the root-mean-square error of the attitude measured using the MSRCKF method is less than 0.3° in static experiments

and the root-mean-square error is less than 0.5° in dynamic experiments.This method can significantly improve the precision of attitude solving compared to EKF and CKF.

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