Unmanned Aerial Vehicle Tracking Using a Particle Filter Based Approach

Motivated by a fixed-speed, fixed-altitude Unmanned Aerial Vehicle (UAV), we seek to control the turning rate of a planar Dubins vehicle that tracks an unpredictable target at a nominal standoff distance. To account for all realizations of the uncertain target kinematics, we model the target motion as a planar random walk. A Bellman equation and an approximating Markov chain that is consistent with the stochastic kinematics is used to compute an optimal control policy that minimizes the expected value of a cost function based on the nominal distance. Our results illustrate that the control can further be applied to a class of continuous, smooth trajectories with no need for further computation.

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Tracking prediction to avoid obstacle path of agricultural unmanned aerial vehicle based on particle filter

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651817710128 ◽

2017 ◽

Vol 232 (4) ◽

pp. 408-416 ◽

Cited By ~ 1

Author(s):

Xihai Zhang ◽

Chengguo Fan ◽

Junlong Fang ◽

Suijia Xu ◽

Jiali Du

Keyword(s):

Particle Filter ◽

Unmanned Aerial Vehicle ◽

Random Noise ◽

Prediction Method ◽

Wind Model ◽

Curvature Variation ◽

Uncertain Disturbances ◽

Aerial Vehicle ◽

Curved Track ◽

Low Altitude

In this article, aiming at the track prediction to avoid obstacles of the plant-protecting unmanned aerial vehicle, first the track points of the curvature variation of non-uniform distribution according to the characters of the track curvature to avoid obstacles were calculated, and the wind model at the low altitude was built. Then, a tracking prediction method of unmanned aerial vehicle was proposed based on the algorithm of particle filter under the situation of uncertain disturbances. Finally, the track to avoid obstacles was divided into curved track and linear track using the bricks mechanism and the tracking prediction was done. The simulated result shows that proposed method can achieve the tracking prediction better with less smaller and better robustness when there are uncertain Gaussian noise, wind speed below 2 m/s as well as error and random noise of the sensor existing.

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Simulation and Analysis of Particle Filter Based Slam System

Annual of Navigation ◽

10.1515/aon-2018-0010 ◽

2018 ◽

Vol 25 (1) ◽

pp. 137-153

Author(s):

Piotr Kaniewski ◽

Paweł Słowak

Keyword(s):

Particle Filter ◽

Unmanned Aerial Vehicle ◽

Simultaneous Localization And Mapping ◽

Flight Trajectory ◽

Localization And Mapping ◽

Aerial Vehicle ◽

Simulation Results ◽

Slam Algorithm

AbstractThe paper describes a problem and an algorithm for simultaneous localization and mapping (SLAM) for an unmanned aerial vehicle (UAV). The algorithm developed by the authors estimates the flight trajectory and builds a map of the terrain below the UAV. As a tool for estimating the UAV position and other parameters of flight, a particle filter was applied. The proposed algorithm was tested and analyzed by simulations and the paper presents a simulator developed by the authors and used for SLAM testing purposes. Chosen simulation results, including maps and UAV trajectories constructed by the SLAM algorithm are included in the paper.

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