TY  - JOUR
T1  - Performance Analysis of Various Artificial Intelligent Neural Networks for GPS/INS Integration
AU - Malleswaran, M. AU - , V. Vaidehi AU - , A. Saravanaselvan AU - , M. Mohankumar 
JO  - International Journal of Soft Computing
VL  - 6
IS  - 5
SP  - 190
EP  - 209
PY  - 2011
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2011.190.209
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2011.190.209
KW  - GPS
KW  -INS
KW  -NPUA
KW  -KF
KW  -AINN
KW  -RBFNN
KW  -BPN
KW  -FCPN
KW  -Full CPN
KW  -ART-CPN
KW  -CNN
KW  -HONN
AB  - Aircraft system mainly relies on Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence due to ephemeric error, satellite clock error, multipath error and signal jamming. To overcome these drawbacks generally GPS is integrated with Inertial Navigation System (INS) mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman Filter (KF) to provide a robust navigation solution. In the KF approach the error model of both INS and GPS are required, this leads to the complexity of the system. This research work presents New Position Update Architecture (NPUA) which consists of various Artificial Intelligent Neural Networks (AINN) that integrates both GPS and INS to overcome the drawbacks in Kalman filter. The various artificial intelligent neural networks that includes both Static and dynamic networks described for the system are Radial Basis Function Neural Network (RBFNN), Back Propagation Neural Network (BPN), Forward only Counter Propagation Neural network (FCPN), Full Counter Propagation Neural network (Full CPN), Adaptive Resonance Theory-Counter Propagation Neural network (ART-CPN), Constructive Neural Network (CNN), Higher Order Neural Networks (HONN) and Input Delayed Neural Networks (IDNN) to predict the INS position error during GPS absence, resulting in different performance. The performance of the different AINNs are analyzed in terms of Root Mean Square Error (RMSE), Performance Index (PI), Number of epochs and Execution Time (ET).
ER  -