Abstract
This paper proposes a new nonlinear state estimator that has a finite impulse response (FIR) structure. The proposed state estimator is called the extended least square unbiased FIR filter (ELSUFF) because it is derived using a least square criterion and has an unbiasedness property. The ELSUFF is a special FIR filter designed for the constant velocity motion model and does not require noise information, such as covariance of Gaussian noise. In situations where noise information is highly uncertain, the ELSUFF can provide consistent performance, while existing nonlinear state estimators, such as the extended Kalman filter (EKF) and the particle filter (PF), often exhibit degraded performance under the same condition. Through simulations, we demonstrate the robustness of the ELSUFF against noise model uncertainty.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J. Kim, K. Cho, and S. Choi, “Lumped disturbance compensation using extended Kalman filter for permanent magnet linear motor system,” International Journal of Control, Automation, and Systems, vol. 14, no. 5, pp. 1244–1253, Oct. 2016. [click]
P. S. Pratama, A. V. Gulakari, Y. D. Setiawan, D. H. Kim, H. K. Kim, and S. B. Kim, “Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules,” International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp. 400–410, Apr. 2016. [click]
H. Li and Y. Shi, “Robust distributed model predictive control of constrained continuous-time nonlinear systems: A robustness constraint approach,” IEEE Trans. on Automatic Control, vol. 59, no. 6, pp. 1673–1678, Jun. 2014.
H. Li and Y. Shi, “Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances,” Automatica, vol. 50, no. 4, pp. 1264–1271, Apr. 2014. [click]
H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning technique and systems,” IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 37, no. 6, pp. 1067–1080, Nov. 2007.
B. F. L. Scala and R. R. Bitmead, “Design of an extended Kalman filter frequency tracker,” IEEE Trans. on Signal Processing, vol. 44, no. 3, pp. 739–742, Mar. 1996.
P. S. Kim and M. E. Lee, “A new FIR filter for state estimation and its application,” Journal of Computer Science and Technology, vol. 22, no. 5, pp. 779–784, Sep. 2007. [click]
C. K. Ahn, S. Han, and W. H. Kwon, “H ∞ FIR filters for linear continuous-time state-space systems,” IEEE Signal Processing Letters, vol. 13, no. 9, pp. 557–560, Sep. 2006.
C. K. Ahn, “A new solution to the induced l ∞ finite impulse response filtering problem based on two matrix inequalities,” International Journal of Control, vol. 87, no. 2, pp. 404–409, Feb. 2014.
C. K. Ahn, P. Shi, and M. V. Basin, “Two-dimensional dissipative control and filtering for Roesser model,” IEEE Trans. on Automatic Control, vol. 60, no. 7, pp. 1745–1759, Jul. 2015.
C. K. Ahn, P. Shi, and M. V. Basin, “Deadbeat dissipative FIR filtering,” IEEE Trans. on Circuits and Systems-I: Regular Papers, vol. 63, no. 8, pp. 1210–1221, Aug. 2016.
J. M. Pak, C. K. Ahn, M. T. Lim, and M. K. Song, “Horizon group shift FIR filter: Alternative nonlinear filter using finite recent measurements,” Measurement, vol. 57, pp. 33–45, Nov. 2014. [click]
J. M. Pak, C. K. Ahn, Y. S. Shmaliy, and M. T. Lim, “Improving reliability of particle filter-based localization in wireless sensor networks via hybrid particle/FIR filtering,” IEEE Trans. on Industrial Informatics, vol. 11, no. 5, pp. 1089–1098, Oct. 2015.
J. M. Pak, C. K. Ahn, Y. S. Shmaliy, P. Shi, and M. T. Lim, “Switching extensible FIR filter bank for adaptive horizon state estimation with application,” IEEE Trans. on Control Systems Technology, vol. 24, no. 3, pp. 1052–1058, May 2016.
J. M. Pak, C. K. Ahn, P. Shi, Y. S. Shmaliy, and M. T. Lim, “Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA-based localization using wireless sensor networks,” IEEE Trans. on Industrial Electronics, to be published, doi: 10.1109/TIE.2016.2608897.
J. M. Pak, C. K. Ahn, Y. S. Shmaliy, P. Shi, and M. T. Lim, “Accurate and reliable human localization using composite particle/FIR filtering,” IEEE Trans. on Human-Machine Systems, to be published, doi: 10.1109/THMS.2016.2611826.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Recommended by Associate Editor Choon Ki Ahn under the direction of Editor Duk-Sun Shim. This research was supported in part by the MSIP (Ministry of Science, ICT, and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8601-16-1003) supervised by the IITP (Institute for Information & communication Technology Promotion) and in part by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610).
Rights and permissions
About this article
Cite this article
Pak, J.M., Kim, P.S., You, S.H. et al. Extended least square unbiased FIR filter for target tracking using the constant velocity motion model. Int. J. Control Autom. Syst. 15, 947–951 (2017). https://doi.org/10.1007/s12555-016-0572-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-016-0572-y