UAV Position Estimation and Collision Avoidance using the Extended Kalman Filter

Chunbo Luo, Sally McClean, G. Parr, Luke Teacy, Renzo De Nardi

Research output: Contribution to journalArticle

69 Citations (Scopus)
179 Downloads (Pure)

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 on-board communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in Least Square (LS) algorithms which are widely used in RSS based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter for distance estimation. Furthermore, the constantly changing path loss factor during UAV flight can also affect the accuracy of estimation. In order 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.
Original languageEnglish
Pages (from-to)2749
JournalIEEE Transactions on Vehicular Technology
Volume62
Issue number6
DOIs
Publication statusPublished - 2013

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