Robust filtering and its application to SINS alignment

2002 ◽  
Author(s):  
Zhang Yanhua ◽  
Cheng Jiabin ◽  
Huang Ziyu
Keyword(s):  
Robust Filtering

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

Intrinsically Optimal Bayesian Robust Filtering

2014 ◽  
Vol 62 (3) ◽  
pp. 657-670 ◽  
Author(s):  
Lori A. Dalton ◽  
Edward R. Dougherty
Keyword(s):  
Robust Filtering

A spatio-temporal noise robust filtering method for separation of 2D incident and reflected irregular waves

2021 ◽  
Vol 237 ◽  
pp. 109544
Author(s):  
Gustavo E. Coelho ◽  
Maria Graça Neves ◽  
António Pascoal ◽  
Álvaro Ribeiro ◽  
Peter Frigaard
Keyword(s):  
Irregular Waves ◽  
Robust Filtering ◽  
Filtering Method ◽  
Temporal Noise ◽  
Spatio Temporal ◽  
Noise Robust

Robust Filtering for Interconnected Uncertain Systems Under Sampled Measurements

1997 ◽  
Vol 119 (2) ◽  
pp. 337-340 ◽  
Author(s):  
Peng Shi ◽  
Youyi Wang ◽  
Lihua Xie
Keyword(s):  
Parameter Uncertainty ◽  
Continuous Time ◽  
Uncertain Systems ◽  
Infinite Horizon ◽  
Riccati Equations ◽  
Robust Filtering ◽  
Sampled Measurements ◽  
Initial States

This paper presents the results of robust filtering for a class of interconnected uncertain systems under sampled measurements. We address the problem of designing filters, using sampled measurements, which would guarantee a prescribed H∞ performance in the continuous-time context, irrespective of the parameter uncertainty and unknown initial states. Both the cases of finite and infinite horizon filtering are investigated in terms of N pairs of Riccati equations with finite discrete jumps.

scholarly journals Robust filtering with stochastic nonlinearities and multiple missing measurements

Automatica ◽  
2009 ◽  
Vol 45 (3) ◽  
pp. 836-841 ◽  
Author(s):  
Guoliang Wei ◽  
Zidong Wang ◽  
Huisheng Shu
Keyword(s):  
Robust Filtering ◽  
Missing Measurements
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