Tracking of the UAV trajectory on the basis of bearing-only observations

This work considers the tracking of the UAV (unmanned aviation vehicle) path on the basis of bearing-only observations including azimuth and elevation angles. The significance of this research becomes clear in the case when GPS either does not work at all or produce the high level of the measurement errors. It is assumed that either UAV's opto-electronic cameras or radar systems are able to capture the angular position of objects with known coordinates and to measure the azimuth and elevation angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others may be used in order to implement these measurements for UAV control. However, all such approximate nonlinear filters produce the estimations with unknown bias and quadratic errors. This peculiarity prevents the data fusion in more or less regular way. Meanwhile, there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings. In this article we develop the modified pseudomeasurement method without bias and with the possibility to evaluate the second moments of the UAV position errors which helps to realize the data fusion. On the basis of this filtering algorithm we develop the control algorithm for tracking of given reference path under external perturbation and noised angular measurements. Modelling examples show the nice performance of the control algorithm.

[1]  Yaakov Bar-Shalom,et al.  Efficient data association for 3D passive sensors: If i have hundreds of targets and ten sensors (or more) , 2011, 14th International Conference on Information Fusion.

[2]  Thiagalingam Kirubarajan,et al.  Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem , 2002, SPIE Defense + Commercial Sensing.

[3]  K. S. Amelin,et al.  An algorithm for refinement of the position of a light UAV on the basis of Kalman filtering of bearing measurements , 2014 .

[4]  Brian D. O. Anderson,et al.  Optimality analysis of sensor-target localization geometries , 2010, Autom..

[5]  Yaakov Bar-Shalom,et al.  Statistical Efficiency of Composite Position Measurements from Passive Sensors , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[6]  V. Pugachev,et al.  Stochastic Differential Systems Analysis and Filtering , 1987 .

[7]  Richard W. Osborne,et al.  Bias estimation for optical sensor measurements with targets of opportunity , 2013, Proceedings of the 16th International Conference on Information Fusion.

[8]  A. Tsourdos,et al.  Robust nonlinear filtering for INS/GPS UAV localization , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[9]  P. B. Sujit,et al.  Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless , 2014, IEEE Control Systems.

[10]  N. Aouf,et al.  Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering , 2010, IEEE Sensors Journal.

[11]  Denis Pillon,et al.  Leg-by-leg Bearings-Only Target Motion Analysis Without Observer Maneuver , 2011, J. Adv. Inf. Fusion.

[12]  P. Strevens Iii , 1985 .

[13]  Jinling Wang,et al.  Adaptive Filter Design for UAV Navigation with GPS/INS/Optic Flow Integration , 2010, 2010 International Conference on Electrical and Control Engineering.

[14]  P. Dunne,et al.  Stochastic Differential Systems , 1988 .

[15]  V. Aidala,et al.  Biased Estimation Properties of the Pseudolinear Tracking Filter , 1982, IEEE Transactions on Aerospace and Electronic Systems.