Gaussian process regression approach for bridging GPS outages in integrated navigation systems

2011 ◽  
Vol 47 (1) ◽  
pp. 52 ◽  
Author(s):  
M.M. Atia ◽  
A. Noureldin ◽  
M. Korenberg
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Regression Approach

scholarly journals A Novel Fault Detection Method for an Integrated Navigation System using Gaussian Process Regression

2016 ◽  
Vol 69 (4) ◽  
pp. 905-919 ◽  
Author(s):  
Yixian Zhu ◽  
Xianghong Cheng ◽  
Lei Wang
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Navigation System ◽  
Detection Method ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Chi Squared

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

scholarly journals A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Giulio Ortali ◽  
◽  
Nicola Demo ◽  
Gianluigi Rozza ◽  
Keyword(s):  
Gaussian Process ◽  
Stokes Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Data Driven ◽  
Regression Approach ◽  
Data Driven Approach ◽  
Naval Engineering ◽  
Proper Orthogonal ◽  
Industrial Problem

<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>

Dealing with Observation Outages within Navigation Data using Gaussian Process Regression

2014 ◽  
Vol 67 (4) ◽  
pp. 603-615 ◽  
Author(s):  
Hongmei Chen ◽  
Xianghong Cheng ◽  
Haipeng Wang ◽  
Xu Han
Keyword(s):  
Kalman Filter ◽  
Theoretical Analysis ◽  
Gaussian Process ◽  
Inertial Navigation System ◽  
Dynamic Models ◽  
Gaussian Process Regression ◽  
Parametric Model ◽  
Integrated Navigation ◽  
Data Set ◽  
Navigation Data

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages.

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