@article{(Open Science Index):https://publications.waset.org/pdf/11104,
	  title     = {Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm},
	  author    = {Ali Asadian and  Behzad Moshiri and  Ali Khaki Sedigh and  Caro Lucas},
	  country	= {},
	  institution	= {},
	  abstract     = {Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {11},
	  year      = {2007},
	  pages     = {3594 - 3597},
	  ee        = {https://publications.waset.org/pdf/11104},
	  url   	= {https://publications.waset.org/vol/11},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 11, 2007},