UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter

• Published in: IEEE transactions on vehicular technology Vol. 62; no. 6; pp. 2749 - 2762
• Main Authors: Chunbo Luo, McClean, Sally I., Parr, Gerard, Teacy, Luke, De Nardi, Renzo
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2013; Institute of Electrical and Electronics Engineers
• Subjects: Adaptation models; Applied sciences; Capacitors. Resistors. Filters; Collision avoidance; Colored noise; Detection, estimation, filtering, equalization, prediction; Electrical engineering. Electrical power engineering; Estimation; Exact sciences and technology; extended Kalman filter (EKF); Information theory; Information, signal and communications theory; Kalman filters; Mathematical model; position estimation; Satellite telecommunications. Space telecommunications; Signal and communications theory; Signal, noise; Telecommunications; Telecommunications and information theory; Various equipment and components; Wireless communication; Positioning; Parameter estimation; Adaptive algorithm; Outdoor installation; Radiolocalisation; Data transmission; Adaptive estimation; Accuracy; Unmanned aerial vehicle; Least squares method; colored noise; On board equipment; GPS system; Color noise; position estimation; Extended Kalman filter; Loss factor; extended Kalman filter (EKF); Satellite navigation; Position measurement; Data fusion; Distance measurement; Transmission loss; Collision avoidance; Strength; Mechanical shock
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2013.2243480

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.