GPS without satellites
Tutor / director / evaluatorAguasca Solé, Alberto
Document typeMaster thesis (pre-Bologna period)
Rights accessOpen Access
Nowadays, there have been great advances in the location technology. The personal positioning oers a very interesting eld of research because the user walking has an unpredictable behaviour and it is dicult to assume predened routes or to take into account other implemented location techniques for vehicles or robots. An approach for integration between inertial navigation systems (INS) and GPS is presented. GPS is a navigation aid accurate and reliable but susceptible to interference like multipath. An INS is very accurate over short periods, but its errors drift unbounded over time. Blending INS with GPS can remedy the performance issues of both. GPS is often combined with other sensors like accelerometers, gyroscopes or magnetometers. The data fusion from these sensors is very important because they allow us to calculate the position and orientation constantly. In this project we are interested in analysing the system behaviour when the signal GPS is unavailable as when the signal is blocked or in indoor environments. The analysis will be carried out through the assessment of a Dead Reckoning algorithm to improve the position information. The system was tested both indoor and outdoor of the Thales building. The personal positioning system is made up of: a receiver GPS, an electronic compass, and the IMU. There are many types of integration methods, and sensors vary greatly, from the complex and expensive, to the simple and inexpensive, in this project it has been used low cost sensors in a loosely coupled approach. A Kalman alter for closed loop integration between GPS and INS is done. The lter propagates and estimates the error states, which are fed back to the INS for correction of the internal navigation states. The integration algorithm has been implemented on Matlab. The algorithm receives the GPS and inertial measurements via serial port to later process all the data. The algorithm has been used to experimentally test and compare navigation performance.