چکیده (انگلیسی):

The recent improvement in Micro-Electro-Mechanical System (MEMS) technology has enabled the
evolvement of Inertial Navigation Unit (INU) to be built on top of a low cost, small size Integrated Circuit
(IC) chip. Due to the nature of the MEMS INU, its outputs are normally corrupted by the resided stochastic
noise. A common practice to regulate its measurements into usable motion data is by fusing the Global
Positioning System (GPS) measurement data with the MEMS INU measurement data through Kalman filter
for position, velocity and orientation estimations. Such integrated system is known as GPS-aided Inertial
Navigation System (INS). Note that the robustness of the GPS-aided INS relies heavily on the availability
of the GPS signals. In the event of no GPS signals, the overall system will solely depend on the INU to
predict the position, velocity and orientation. The prediction results will eventually drift from its true value
due to the INU’s resided stochastic noise. In this study, a remedy system using Adaptive Neuro-Fuzzy
Inference System (ANFIS) is developed to improve the performance of the GPS-aided INS during GPS
outage condition. UAV motion sensing experiment was carried out and GPS outage conditions were
imposed at several locations during the UAV navigation. The motion prediction dataduring GPS outages,
with and without ANFIS implementation, were compared and the results clearly show that the GPS-aided
INS with ANFIS implementation achieved better performance than the GPS-aided INS without ANFIS.