Robust Filtering for Fine Alignment on the Moving Base for SINS

INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences ◽

10.4156/aiss.vol4.issue14.19 ◽

2012 ◽

Vol 4 (14) ◽

pp. 158-167 ◽

Cited By ~ 1

Author(s):

Jin Feng ◽

Fei Yu ◽

Wei Gao ◽

HeMing Jia

Keyword(s):

Robust Filtering ◽

Moving Base

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A study of moving base simulation motion cues utilizing washout technique

PsycEXTRA Dataset ◽

10.1037/e573672012-006 ◽

1975 ◽

Author(s):

Mark Kirkpatrick ◽

Nicholas Shields ◽

Ronald Brye ◽

Frank L. Vinz

Keyword(s):

Motion Cues ◽

Moving Base

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Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽

10.3390/s21041250 ◽

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.

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A sensitive micromachined resonant accelerometer for moving-base gravimetry

Sensors and Actuators A Physical ◽

10.1016/j.sna.2021.112694 ◽

2021 ◽

pp. 112694

Author(s):

Zhengxiang Fang ◽

Yonggang Yin ◽

Chen Chen ◽

Shujuan Zhang ◽

Yunfeng Liu ◽

...

Keyword(s):

Moving Base ◽

Resonant Accelerometer

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Manipulator Motion Planning for Part Pickup and Transport Operations From a Moving Base

IEEE Transactions on Automation Science and Engineering ◽

10.1109/tase.2020.3020050 ◽

2020 ◽

pp. 1-16

Author(s):

Shantanu Thakar ◽

Pradeep Rajendran ◽

Ariyan M. Kabir ◽

Satyandra K. Gupta

Keyword(s):

Motion Planning ◽

Moving Base

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Recursive Robust Filtering with Finite-Step Correlated Process Noises and Missing Measurements

Circuits Systems and Signal Processing ◽

10.1007/s00034-011-9289-6 ◽

2011 ◽

Vol 30 (6) ◽

pp. 1355-1368 ◽

Cited By ~ 21

Author(s):

Jianxin Feng ◽

Zidong Wang ◽

Ming Zeng

Keyword(s):

Robust Filtering ◽

Missing Measurements ◽

Finite Step

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Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.2802 ◽

2012 ◽

Vol 433-440 ◽

pp. 2802-2807

Author(s):

Ying Hong Han ◽

Wan Chun Chen

Keyword(s):

Kalman Filter ◽

Inertial Navigation ◽

Navigation Systems ◽

Misalignment Angle ◽

Inertial Navigation Systems ◽

Excellent Performance ◽

Transfer Alignment ◽

Matching Methods ◽

Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

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