UAV position estimation and collision avoidance using the extended kalman filter

Luo, C., McClean, S.I., Parr, G., Teacy, L. and De Nardi, R. (2013) UAV position estimation and collision avoidance using the extended kalman filter. IEEE Transactions on Vehicular Technology, 62 (6). pp. 2749-2762. ISSN 0018-9545

Full text not available from this repository. (Request a copy)

Abstract

Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 00:33
Last Modified: 22 Apr 2020 01:40
URI: https://ueaeprints.uea.ac.uk/id/eprint/60095
DOI: 10.1109/TVT.2013.2243480

Actions (login required)

View Item View Item