Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial se...

Time of Arrival (TOA) observations from local beacon signals can be used to augment and provide a back-up navigation source for GPS signals in a Software Defined Radio (SDR). The addition of inertial sensor inputs to the SDR offers the ability to track down to much lower power levels for both the GPS and TOA signals, effectively deal with multipath, and recover more quickly from signal outages....

This paper presents the development of GPS based navigation system for portable devices at Tampere University of Technology. The objective was to develop a fully integrated system using MEMS inertial sensors with price range of US$ 10-15 for the set of inertial sensors combined with low-update rate GPS measurements. This system doesn’t require vehicle installation and can be easily transferred ...

Nonlinear Kalman filtering methods are the most popular algorithms for integration of a MEMS-based inertial measurement unit (MEMS-IMU) with a global positioning system (GPS). Despite their accuracy, these nonlinear algorithms present a challenge in terms of the computational efficiency for portable wearable devices. We introduce a cascaded Kalman filter for GPS/MEMS-IMU integration for the pur...

Many land vehicles are presently equipped with on-board navigation systems that currently rely entirely on the availability and accuracy of GPS. The accessibility of low cost MEMS-based inertial measurement units (IMU) can be used to provide precise position and velocity data for GPS signal acquisition and reacquisition after outages. This reduces the time and the search domain required for det...

This article presents an inexpensive inertial attitude and position reference system for a small unmanned aerial vehicle (UAV) that utilizes low cost inertial sensors in conjunction with a global positioning system (GPS) sensor. The attitude estimates are obtained from a complementary filter and a Kalman filter by combining the measurements from the inertial sensors with the supplementary attit...