A low-cost INS/GPS integration methodology based on random forest regression
- 1 September 2013
- journal article
- Published by Elsevier BV in Expert Systems with Applications
- Vol. 40 (11), 4653-4659
- https://doi.org/10.1016/j.eswa.2013.02.002
Abstract
No abstract availableThis publication has 14 references indexed in Scilit:
- A new source difference artificial neural network for enhanced positioning accuracyMeasurement Science and Technology, 2012
- Knowledge-based neural network approaches for modeling and estimating radon concentrationsEnvironmental Progress & Sustainable Energy, 2012
- RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGYEcology, 2007
- The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning InstrumentsIEEE Transactions on Instrumentation and Measurement, 2006
- Newer classification and regression tree techniques: Bagging and random forests for ecological predictionEcosystems, 2006
- Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genesBMC Bioinformatics, 2004
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002
- Random ForestsMachine Learning, 2001
- Training the random neural network using quasi-Newton methodsEuropean Journal of Operational Research, 2000
- Nonlinear Kalman filtering techniques for terrain-aided navigationIEEE Transactions on Automatic Control, 1983