A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Authors:
Deepak Bhatt;Priyanka Aggarwal;Vijay Devabhaktuni;Prabir Bhattacharya
Affiliations:
EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;EECS Department, University of Toledo, MS 308, 2801 W. Bancroft St., Toledo, OH 43606, United States;School of Computing Sciences and Informatics, University of Cincinnati, Cincinnati, OH 45221, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

Citing 10
Cited 0

The transferable belief model

Artificial Intelligence
The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel

Neural Computation
A tutorial on support vector regression

Statistics and Computing
Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

Engineering Applications of Artificial Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Information combination operators for data fusion: a comparative review with classification

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the Dempster-Shafer evidence theory and non-hierarchical aggregation of belief structures

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A low-cost INS/GPS integration methodology based on random forest regression

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises. The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster-Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20-87% improvement in the positional accuracy was found against ANN and RFR.