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Comparative Out-of-Sequence Estimation Techniques for Multi-Sensor Target Tracking

[+] Author Affiliations
Clara Mosquera-Lopez, Daniel Pack

University of Texas at San Antonio, San Antonio, TX

Paper No. DSCC2014-5863, pp. V002T26A001; 5 pages
doi:10.1115/DSCC2014-5863
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME

abstract

In this paper, performance comparisons are carried out between two out-of-sequence estimation filtering techniques based on the principles of the Extended Kalman Filter (EKF) and the Sigma-point Kalman filter (SPKF), in a mobile platform tracking application where distributed radars are used to estimate both linear and highly nonlinear movements of an aircraft. Two scenarios were considered: 1) aircraft movements fit a white noise acceleration model; and 2) aircraft movement follows a coordinated turn model with unknown turn rate. In addition, we evaluate the individual performance of the out-of-order filters against the ideal cases obtained by running the EKF and SPKF with reordered measurements in a chronological sequence. Simulation results show that the algorithms used for dealing with out-of-sequence measurements closely resemble the performance of the non-out-of-order filters. In terms of estimation accuracy, the out-of-order algorithm based on the SPKF outperforms the one based on the EKF when a highly nonlinear aircraft movement is observed. For nearly linear systems, there is not a significant difference between the two approaches.

Copyright © 2014 by ASME

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