Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 219-225, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
26 Aug 2015
A. Masiero, F. Fissore, A. Guarnieri, F. Pirotti, and A. Vettore Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Viale dell’Università 16, Legnaro (PD) 35020, Italy
Keywords: UAV, Positioning, Collision avoidance, RPAS, Statistical filtering Abstract. In recent years, Unmanned Aerial Vehicles (UAVs) are attracting more and more attention in both the research and industrial communities: indeed, the possibility to use them in a wide range of remote sensing applications makes them a very flexible and attractive solution in both civil and commercial cases (e.g. precision agriculture, security and control, monitoring of sites, exploration of areas difficult to reach).

Most of the existing UAV positioning systems rely on the use of the GPS signal. Despite this can be a satisfactory solution in open environments where the GPS signal is available, there are several operating conditions of interest where it is unavailable or unreliable (e.g. close to high buildings, or mountains, in indoor environments). Consequently, a different approach has to be adopted in these cases.

This paper considers the use ofWiFi measurements in order to obtain position estimations of the device of interest. More specifically, to limit the costs for the devices involved in the positioning operations, an approach based on radio signal strengths (RSS) measurements is considered.

Thanks to the use of a Kalman filter, the proposed approach takes advantage of the temporal dynamic of the device of interest in order to improve the positioning results initially provided by means of maximum likelihood estimations. The considered UAVs are assumed to be provided with communication devices, which can allow them to communicate with each other in order to improve their cooperation abilities. In particular, the collision avoidance problem is examined in this work.

Conference paper (PDF, 918 KB)

Citation: Masiero, A., Fissore, F., Guarnieri, A., Pirotti, F., and Vettore, A.: UAV POSITIONING AND COLLISION AVOIDANCE BASED ON RSS MEASUREMENTS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 219-225,, 2015.

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