Improved robust Kalman filter for state model errors in GNSS-PPP/MEMS-IMU double state integrated navigation

2021 ◽  
Vol 67 (10) ◽  
pp. 3156-3168
Author(s):  
Zengke Li ◽  
Zan Liu ◽  
Long Zhao
Keyword(s):  
Kalman Filter ◽  
State Model ◽  
Integrated Navigation ◽  
Model Errors ◽  
Robust Kalman Filter ◽  
Mems Imu

Online Dynamic Response Reconstruction in the Presence of Observation Outliers

2021 ◽  
Author(s):  
Chaodong Zhang ◽  
Jian’an Li ◽  
Youlin Xu
Keyword(s):  
Kalman Filter ◽  
Dynamic Response ◽  
Numerical Study ◽  
Frame Structure ◽  
System Model ◽  
Measurement Information ◽  
Model Errors ◽  
Process Noise ◽  
Robust Kalman Filter ◽  
Response Reconstruction

Previous studies show that Kalman filter (KF)-based dynamic response reconstruction of a structure has distinct advantages in the aspects of combining the system model with limited measurement information and dealing with system model errors and measurement Gaussian noises. However, because the recursive KF aims to achieve a least-squares estimate of state vector by minimizing a quadratic criterion, observation outliers could dramatically deteriorate the estimator’s performance and considerably reduce the response reconstruction accuracy. This study addresses the KF-based online response reconstruction of a structure in the presence of observation outliers. The outlier-robust Kalman filter (OKF), in which the outlier is discerned and reweighted iteratively to achieve the generalized maximum likelihood (ML) estimate, is used instead of KF for online dynamic response reconstruction. The influences of process noise and outlier duration to response reconstruction are investigated in the numerical study of a simple 5-story frame structure. The experimental work on a simply-supported overhanging steel beam is conducted to testify the effectiveness of the proposed method. The results demonstrate that compared with the KF-based response reconstruction, the proposed OKF-based method is capable of dealing with the observation outliers and producing more accurate response construction in presence of observation outliers.

scholarly journals A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51386-51395 ◽  
Author(s):  
Li Luo ◽  
Yonggang Zhang ◽  
Tao Fang ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Robust Kalman Filter

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

scholarly journals A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5909
Author(s):  
Guangle Gao ◽  
Shesheng Gao ◽  
Genyuan Hong ◽  
Xu Peng ◽  
Tian Yu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Reliability ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Chi Square ◽  
Integrated Navigation System ◽  
Robust Kalman Filter ◽  
Chi Square Test ◽  
Spectral Redshift

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

GPS/UWB/MEMS-IMU tightly coupled navigation with improved robust Kalman filter

2016 ◽  
Vol 58 (11) ◽  
pp. 2424-2434 ◽  
Author(s):  
Zengke Li ◽  
Guobin Chang ◽  
Jingxiang Gao ◽  
Jian Wang ◽  
Alberto Hernandez
Keyword(s):  
Kalman Filter ◽  
Robust Kalman Filter ◽  
Tightly Coupled ◽  
Mems Imu

An improved Sage Husa adaptive robust Kalman Filter for de-noising the MEMS IMU drift signal

2018 ◽  
Author(s):  
Mundla Narasimhappa ◽  
Arun D. Mahindrakar ◽  
Vitor C. Guizilini ◽  
Marco H Terra ◽  
Samrat L Sabat
Keyword(s):  
Kalman Filter ◽  
Robust Kalman Filter ◽  
Mems Imu

scholarly journals An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

2021 ◽  
Vol 13 (21) ◽  
pp. 4317
Author(s):  
Peihui Yan ◽  
Jinguang Jiang ◽  
Fangning Zhang ◽  
Dongpeng Xie ◽  
Jiaji Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Covariance Matrix ◽  
Urban Environments ◽  
Single Frequency ◽  
Low Power Consumption ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Mems Imu ◽  
Observation Vector

Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.

Sign in / Sign up

Export Citation Format

Share Document