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• W2491696768 abstract "Since Kalman introduced his optimal estimation filter in 1960, the Kalman Filter (KF) has been widely used in different applications. In navigation applications, KF has proven to be the most optimal filtering technique, and therefore, it has been implemented for years to estimate and compensate the corresponding errors in the Global Positioning System (GPS), the Inertial Navigation System (INS) and the INS/GPS integrated systems. For INS/GPS navigation, several KF approaches have been used such as the Linearized KF (LKF), the Extended KF (EKF) and recently the Unscented KF (UKF). In general, the choice of the KF approach depends on the INS quality, INS/GPS integration type, quality of GPS measurements, initial conditions and the application at hand. However, all KF approaches are tunable in a way that several parameters can be changed inside the KF. When the IMU is working as a stand-alone navigation system, during GPS signal blockage periods, some of these parameters will have a significant effect on the obtained navigation accuracy. In this paper, a sensitivity analysis of these parameters in INS/GPS integrated systems will be performed. The analysis will address the different KF parameters that are related to GPS update measurements quality, IMU quality, stochastic modeling of inertial sensors and inertial sensor noise. These analyses are carried out using two land-vehicle INS/GPS kinematic tests. Several Inertial Measuring Units (IMUs) of different grades are used in these tests including high-quality, medium quality and low-quality IMUs. For each IMU, the sensitivity analysis will be preformed using different KF approaches during several induced GPS signal blockage periods. The results of all analyzed cases are presented and discussed." @default.
• W2491696768 created "2016-08-23" @default.
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• W2491696768 date "2006-01-20" @default.
• W2491696768 modified "2023-09-24" @default.
• W2491696768 title "INS/GPS Sensitivity Analysis Using Different Kalman Filter Approaches" @default.
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