This paper explains and compares the different methods that could be used to characterize and calibrate COTS sensors onboard a 2U class nanosatellite. Attitude sensors are used in satellite missions for determination of attitude in orbit and then using this information for controlling the satellite for effective payload action. The paper focuses on the two major sensors on board; the Anisotropic Magnetoresistance (AMR) Magnetometer and a Micro-Electro-Mechanical Systems (MEMS) Gyroscope. The COTS sensors tend to have manufacturing defects which lead to internal errors and external factors like temperature adding to the deviation from the true value. A robust explanation of the factors affecting the sensor values and a corresponding functioning of the mathematical model built on the various internal sources of error and external stimulants which affect the output of these sensors is provided. In contrast to mathematical modeling, an offboard neural network uses the method of backpropagation for defining a nonlinear relationship between the raw sensor values and the actual values. The calibrated values obtained from the application of the mathematical error model and the neural network is presented through a series of graphs. Further critical analyses of the plots are done to obtain the best method to calibrate the particular sensor. This systematic calibration aids in improving the attitude estimation and control design of the satellite further leading to enhanced control on payload action. This enables the low-cost COTS sensors to be used in aerospace applications.
Analysis and Comparison of Calibration Techniques for COTS Sensors Onboard a Nanosatellite
01.03.2019
4657263 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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