Diplomová práce se zabývá návrhem algoritmu rozšířeného Kalmanova filtru, který integruje data z inerciálního navigačního systému (INS) a globálního polohovacího systému (GPS). Součástí algoritmu je i samotná mechanizace INS, určující na základě dat z akcelerometrů a gyroskopů údaje o rychlosti, zeměpisné pozici a polohových úhlech letadla. Vzhledem k rychlému nárůstu chybovosti INS je výstup korigován hodnotami rychlosti a pozice získané z GPS. Výsledný algoritmus je implementován v prostředí Simulink. Součástí práce je odvození jednotlivých stavových matic rozšířeného Kalmanova filtru.

This thesis investigates the feasibility and performance of an inertial based positioning system for dairy cows in a barn environment. The investigated positioning method is pedestrian dead reckoning using inertial navigation with MEMS sensors. While this method is well known for human positioning applications, there has not been a lot of studies of its use on terrestrial animals. Since inertial based positioning systems are dependent on drift correction, the focus of the research is drift correction methods. Two methods, zero velocity update (ZUPT) and sparse locations, are compared with regards to positioning accuracy, energy consumption and sensor placement.  The best positioning estimates are achieved by using ZUPT corrections at a sample rate of 10 Hz, resulting in a mean position drift of 0.2145 m=m. Using a proposed equidistant sample time based sleep mode scheme, this would require a theoretical supply current of 0.21 mA. It is also seen that better position estimates are obtained for sensors that are placed low and on the front legs. The sparse locations method suffers from severe position drift between the locations, resulting in unusable positioning data. A combination of ZUPT and sparse location yields less accurate positioning than ZUPT only. / Denna masteruppsats undersöker genomförbarhet och prestanda av ett tröghetsbaserat positioneringsssystem för mjölkkor i en lada. Den undersökta metoden är död räkning för fotgängare mha. tröghetsnavigering med MEMSsensorer. Denna metod är välkänd för positionering av människor, men få studier har gjorts kring dess användbarhet för djur. Eftersom tröghetsbaserad navigering är beroende av driftkorrigering är detta fokuset för forskningen. Två olika metoder utvärderas, zero velocity update (ZUPT) och sparse locations, och en jämförelse görs med avseende på positionsnoggrannhet, energiförbrukning och sensorplacering.Bäst positionering uppnås med ZUPT-korrigeringar vid en samplingsfrekvens på 10 Hz, vilket ger ett medelvärde av positionsdrift på 0.2145 m=m. Om ett föreslaget ekvidistant samplingstidsbaserat schema för viloläge används skulle 10 Hz kräva en teoretisk matningsström på 0.21 mA. Vidare fås bättre positioneringsresultat för sensorer som är placerade lågt och på frambenen. Korrektionsmetoden med sparse locations ger en svår positionsdrift mellan platserna, vilket resulterar i oanvändbar positionsdata. En kombination av ZUPT och sparse locations ger sämre precision än om endast ZUPT används, samt ökar energiförburkningen på grund av behovet av ytterligare sensorer.

Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are sensors that are widely used for land vehicle navigation. GPS receivers provide position and/or velocity data to any user on the Earth&rsquo / s surface independent of his position. Yet, there are some conditions that the receiver encounters difficulties, such as weather conditions and some blockage problems due to buildings, trees etc. Due to these difficulties, GPS receivers&rsquo / errors increase. On the other hand, IMU works with respect to Newton&rsquo / s laws. Thus, in stark contrast with other navigation sensors (i.e. radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this feature, IMU is used in almost all navigation applications. However, it has some disadvantages such as possible alignment errors, computational errors and instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.). Therefore, a fusion or integration of GPS and IMU provides a more accurate navigation data compared to only GPS or only IMU navigation data. v In this thesis, loosely coupled GPS/IMU integration systems are implemented using feed forward and feedback configurations. The mechanization equations, which convert the IMU navigation data (i.e. acceleration and angular velocity components) with respect to an inertial reference frame to position, velocity and orientation data with respect to any desired frame, are derived for the geographical frame. In other words, the mechanization equations convert the IMU data to the Inertial Navigation System (INS) data. Concerning this conversion, error model of INS is developed using the perturbation of the mechanization equations and adding the IMU&rsquo / s sensor&rsquo / s error model to the perturbed mechanization equation. Based on this error model, a Kalman filter is constructed. Finally, current navigation data is calculated using IMU data with the help of the mechanization equations. GPS receiver supplies external measurement data to Kalman filter. Kalman filter estimates the error of INS using the error mathematical model and current navigation data is updated using Kalman filter error estimates. Within the scope of this study, some real experimental tests are carried out using the software developed as a part of this study. The test results verify that feedback GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In addition, some tests are carried out to observe the results when the GPS receiver&rsquo / s data lost. In these tests also, the feedback GPS/INS integration is observed to have better performance than the feed forward GPS/INS integration.

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This diploma thesis deals with processing of measured data from inertial navigation system in order these could be used for stabilization. There is general information about aerial vehicles called copters with emphasis on four-rotor construction called quadrocopter at first. Then mathematical model of quadrocopter in state space form is derived, the particular implementation of university developed quadrocopter is described and the design of data processing algorithm is presented with measured results. Finally achieved results are discussed.

This paper deals with study of inertial navigation, global navigation satellite system, and their fusion into the one navigation solution. The first part of the work is to calculate the trajectory from accelerometers and gyroscopes measurements. Navigation equations calculate rotation with quaternions and remove gravity sensed by accelerometers. The equation’s output is in earth centred fixed navigation frame. Then, inertial navigation errors are discussed and focused to the bias correction. Theory about INS/GNSS inte- gration compares different integration architecture. The Kalman filter is used to obtain navigation solution for attitude, velocity and position with advantages of both systems.