Paper Info
Title
Dempster Shafer neural network algorithm for land vehicle navigation application
Abstract
A Global Positioning System (GPS)-aided Inertial Navigation System (INS) provides a continuous navigation solution with reduced uncertainty and ambiguity. Bayesian approaches like Extended Kalman filter or Particle filter are generally developed for fusing the GPS and INS data. However, these techniques require prior distribution (representing the degree of belief) to be accurately defined for all incorporated parameters-whether known or unknown. If no previous knowledge is obtainable, equal probabilities are assigned to all events, which is questionable. To overcome these limitations, Dempster Shafer (DS) evidence theory is implemented in this paper. In order to effectively fuse GPS and INS data for land vehicle navigation application, we propose an efficient Dempster Shafer Neural Network (DSNN) algorithm by integrating the Dempster Shafer theory and the artificial neural network. Our field test results clearly indicate that the proposed DSNN algorithm effectively compensated and reduced positional inaccuracies during no GPS outage and GPS outage conditions for low cost inertial sensors.
Year
DOI
Venue
2013
10.1016/j.ins.2013.08.039
Inf. Sci.
Keywords
Field
DocType
efficient dempster shafer neural,global positioning system,dempster shafer theory,fuse gps,dempster shafer,extended kalman filter,gps outage condition,inertial navigation system,ins data,land vehicle navigation application,gps outage,dempster shafer neural network,artificial neural network
Inertial navigation system,Extended Kalman filter,Particle filter,Global Positioning System,Inertial measurement unit,Artificial intelligence,Artificial neural network,Prior probability,Dempster–Shafer theory,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
253,
0020-0255
14
PageRank 
References 
Authors
0.64
13
4
Name
Order
Citations
PageRank
Priyanka Aggarwal1514.56
Deepak Bhatt2472.86
Vijay Devabhaktuni312415.65
Prabir Bhattacharya41010147.90