The development of SOTM systems for land applications increases the need of use greater frequency (Ka band) : this introduces new problems related to land systems SOTM that do not appear in aeronautics applications. Among these, satellite tracking/pointing is a very critical aspect, above all in Ka band for which the maximum pointing error admitted is one tenth of degree: this does not allow the use of wide step tracking technique and it foist on the use of accurate open loop systems making use of inertial measurement system. In order to improve the performance of this system and making it accurate for Ka band, novel solution in the prediction of the vehicle attitude must be studied. In this paper the performance of inertial measurement system are enhanced by means of a fusion of sensor data and extended Kalman filters.

Coco, S., Chisari, G., Falco, P.D., Iraci, E., Militello, S., & Laudani, A. (2014). Accurate estimation of vehicle attitude for satellite tracking in ka band SOTM. In Proceedings - UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014 (pp.409-414). Institute of Electrical and Electronics Engineers Inc. [10.1109/EMS.2014.57].

Accurate estimation of vehicle attitude for satellite tracking in ka band SOTM

LAUDANI, ANTONINO
2014

Abstract

The development of SOTM systems for land applications increases the need of use greater frequency (Ka band) : this introduces new problems related to land systems SOTM that do not appear in aeronautics applications. Among these, satellite tracking/pointing is a very critical aspect, above all in Ka band for which the maximum pointing error admitted is one tenth of degree: this does not allow the use of wide step tracking technique and it foist on the use of accurate open loop systems making use of inertial measurement system. In order to improve the performance of this system and making it accurate for Ka band, novel solution in the prediction of the vehicle attitude must be studied. In this paper the performance of inertial measurement system are enhanced by means of a fusion of sensor data and extended Kalman filters.
9781479974115
9781479974115
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/300543
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