NESIS
NESIS on yhden laatikon ratkaisu ultrakevyeeseen lentokoneeseen, itserakennettuun lentokoneeseen, autogyroon tai helikopteriin.
Lue enemmän aiheista:
AHRS taustaa
Below are the key features of the system.
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* UAV navigation principles are applied - inertial navigation serves as basis navigation which is aided by GPS data. The integration of both worlds is obtained by using Kalman filtering.
- * Solid state sensors: 3 axis MEMS accelerometers and angular rates, 3 axis magnetometer, pressures sensors, temperature sensors are used in inertial navigation. The sensor reading is performed at 100 Hz, while attitude and position is calculated at 40 Hz.
- * 12 channel GPS aids the inertial navigation unit when the GPS signal is available. Although GPS is not the main source of the navigation it is essential for calibration of inertial navigation results.
- * Loss of the GPS signal is tolerated for several minutes without loss of the situation awareness. The inertial navigation does not need GPS signal to calculate current position but it uses GPS merely to compare inertial and GPS solution. In general, the GPS gives better long term position prediction, while inertial navigation gives better short term position prediction. In the case of bad GPS signal, the accumulation of short term prediction errors is still within reasonable limits for several minutes. Once the GPS signal is back, both solutions are merged into one.
- * World wide magnetic field declination and inclination model is built into the system in a seamless way. The magnetic declination and inclination is updated automatically.
- * Gyro-stabilized magnetic compass. Basic heading information is obtained from integration of the angular rate sensors, which is compared with the magnetic heading solution from the 3-axis magnetometer.
- * Rigorous calibration over all temperature range. The bad side of solid state sensors is their sensitivity towards temperature change. Therefore, special care is taken in the sensor calibration process, where each sensor is calibrated over its all range at different temperatures.
- * Excellent wind model. No tips and tricks are needed to get correct wind data. Since the wind is used as a state variable in the Kalman filter, the results of wind strength and direction are obtained directly from the filter in a similar manner as attitude and position.