Sharif Digital Repository

The aim of this paper is to design an inertial navigation system (INS) for use in a geometry pipe inspection gauge, capable of measuring pipeline movements and producing the line's 3D map with a reasonable accuracy. A suitable reference path was generated as a design platform. Solving the navigation equations and compensating for the errors, by using extended Kaiman filter (EKF) approach, the INS path was generated and its position errors in all three directions were considered. Divergence problems due to far apart GPS position observations, was overcome by defining suitable threshold for the variances of the estimated errors. Copyright © 2007 by ASME  

By development of ocean exploration, autonomous vehicles are employed to perform on-water and underwater tasks. Using an extended Kalman filter, Inertial Navigation System/Doppler Velocity Log (INS/DVL) integrated systems are trying to navigate in oceans and underwater environments when Global Positioning System (GPS) signals are not accessible. The dependency of DVL signals on acoustic environments may cause any DVL malfunction due to sea creatures or strong wave-absorbing material. In this paper, an improved version of evolutionary TS-fuzzy (eTS) is proposed in order to predict DVL sensor outputs at DVL malfunction moment, by utilizing an artificial intelligent (AI) aided integrated... 

In this research, Model Aided Inertial Navigation (MAIN) is used during the automatic landing of an Unmanned Aerial Vehicle (UAV). A new MAIN algorithm is proposed which is fast and accurate enough to be used in this phase. In this algorithm, the six Degree of Freedom (6DoF) flight simulation of the UAV is integrated with the Inertial Navigation System (INS) such that the 6DoF model acts as an aiding system for the INS. In the last parts of the landing phase, when the UAV flies in proximity of the ground surface, the proposed integrated navigation system can estimate the altitude of UAV utilizing the “ground effect” phenomenon. Therefore, the method does not have the drawbacks of active... 

In today's world, control and navigation of autonomous underwater vehicles (AUVs) are quite challenging issues. In these fields, obtaining an identified dynamic model of AUV is a vital part. In this paper, a method for parameter estimation of an AUV planar model is proposed, which uses augmented state space technique and Square Root Transformed Unscented Kalman Filter (SR-TUKF) as an estimator. Furthermore, by modeling, misalignment between Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) is estimated, simultaneously. Parameter identification is conducted using data of an AUV, equipped with Gyroscope, DVL and Encoder for measuring control inputs, in a planar maneuver. According... 

Nonlinear filtering techniques are used to fuse the Global Positioning System (GPS) with Inertial Navigation System (INS) to provide a robust and reliable navigation system with a performance superior to that of either INS or GPS alone. Prominent nonlinear estimators in this field are Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF, and PF based on EKF and UKF in an INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation, and bias/scale factor estimation are investigated using these four filtering...