A Comparative Performance Analysis of Filtering and Smoothing Techniques on a Simulated Unmanned Aircraft System
نویسندگان
چکیده
In order for unmanned aircraft systems (UAS) to fly safely into civil airspace, the development of vigorous unmanned aircraft’s Accident Avoidance System (UAS-AAS) is rather crucial. However, accurate localization is a key prerequisite for a UAS-AAS to be successful. This paper investigate the application of two filtering and smoothing techniques to a UAS given the aircraft dynamics, sensor performance, and the environment in which the aircraft operate. The navigational system provides data of position, velocity and noise. Navigational data is randomly generated. The estimated position of the UAS is then determined by applying the Kalman filtering, Kalman smoothing, Particle filtering and Particle smoothing. The experimental result shows that irrespective of the errors in the measurements, both techniques perform well in estimating the true position of the system. However, the smoothing techniques show the higher accuracy of the results due to the use of more future data than filtering technique.
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تاریخ انتشار 2014