Abstract and Applied Analysis

A Filtering Algorithm for Maneuvering Target Tracking Based on Smoothing Spline Fitting

Yunfeng Liu, Jidong Suo, Hamid Reza Karimi, and Xiaoming Liu

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


Maneuvering target tracking is a challenge. Target’s sudden speed or direction changing would make the common filtering tracker divergence. To improve the accuracy of maneuvering target tracking, we propose a tracking algorithm based on spline fitting. Curve fitting, based on historical point trace, reflects the mobility information. The innovation of this paper is assuming that there is no dynamic motion model, and prediction is only based on the curve fitting over the measured data. Monte Carlo simulation results show that, when sea targets are maneuvering, the proposed algorithm has better accuracy than the conventional Kalman filter algorithm and the interactive multiple model filtering algorithm, maintaining simple structure and small amount of storage.

Article information

Abstr. Appl. Anal., Volume 2014 (2014), Article ID 127643, 6 pages.

First available in Project Euclid: 2 October 2014

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Liu, Yunfeng; Suo, Jidong; Karimi, Hamid Reza; Liu, Xiaoming. A Filtering Algorithm for Maneuvering Target Tracking Based on Smoothing Spline Fitting. Abstr. Appl. Anal. 2014 (2014), Article ID 127643, 6 pages. doi:10.1155/2014/127643. https://projecteuclid.org/euclid.aaa/1412273154

Export citation


  • R. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, pp. 35–45, 1960.
  • D. T. Magill, “Optimal adaptive estimation of sampled stochastic processes,” IEEE Transactions on Automatic Control, vol. 10, no. 4, pp. 434–439, 1965.
  • D. G. Lainiotis, “Optimal adaptive estimation: structure and parameter adaptation,” IEEE Transactions on Automatic Control, vol. 16, no. 2, pp. 160–170, 1971.
  • H. Blom, “An efficient filter for abruptly changing systems,” in Proceedings of the 23rd IEEE Conference on Decision and Control, pp. 656–658, 1984.
  • H. A. P. Blom and Y. Bar-Shalom, “Interacting multiple model algorithm for systems with Markovian switching coefficients,” IEEE Transactions on Automatic Control, vol. 33, no. 8, pp. 780–783, 1988.
  • E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, “Interacting multiple model methods in target tracking: a survey,” IEEE Transactions on Aerospace and Electronic Systems, vol. 34, no. 1, pp. 103–123, 1998.
  • X. R. Li, “Multiple-model estimation with variable structure: some theoretical considerations,” in Proceedings of the 33rd IEEE Conference on Decision and Control, pp. 1199–1204, December 1994.
  • Y. Wan, S. Y. Wang, and X. Qin, “IMM iterated extended ${H}_{\infty }$ particle filter algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 970158, 8 pages, 2013.
  • R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” Journal of Basic Engineering, vol. 83, pp. 95–108, 1961.
  • S. Sage and G. Husa, “Adaptive filtering with unkown prior statistics,” in Proceedings of the Joint Automatic Control Conference, pp. 760–769, 1969.
  • R. K. Mehra, “Approaches to adaptive filtering,” IEEE Transactions on Automatic Control, vol. 17, no. 5, pp. 693–698, 1972.
  • X. Kan, H. S. Shu, and Y. Che, “Asymptotic parameter estimation for a class of linear stochastic systems using Kelman-Bucy filtering,” Mathematical Problems in Engineering, vol. 2012, Article ID 342705, 15 pages, 2012.
  • Z. Ma, “Spacecraft attitude determination by adaptive Kalman filtering,” in Proceedings of the 23rd Chinese Control Conference, pp. 255–259, 2004.
  • L. Weiss, “A survey of discrete Kalman-Bucy filtering with unknown noise covariance,” in Proceedings of the Control and Flight Mechanics Conference, pp. 17–19, 1970.
  • E. Lichtfuss, Non-GPS Navigation Using Vision-Aiding and Active Radio Range Measurements, Department of the Air Force Air University, Air Force Institute of Technology, Wright-Patterson Air Force Base, Wright-Patterson, Ohio, USA, 2011.
  • W. Su, C. Huang, P. Liu, and M. Ma, “Application of adaptive Kalman filter technique in initial alignment of inertial navigation system,” Journal of Chinese Inertial Technology, vol. 18, no. 1, pp. 44–47, 2010.
  • H. Fu, Y. Wu, and T. Lou, “Adaptive unscented incremental filter method,” Journal of Aerospace Power, vol. 28, no. 2, pp. 259–263, 2013.
  • K. W. Chiang, C. Lin, and K. Y. Peng, “The performance analysis of an AKF based tightly coupled INS/GNSS sensor fusion scheme with non-holonomic constraints for land vehicular applications,” Innovation for Applied Science and Technology, vol. 284–287, pp. 1956–1960, 2012.
  • Q. Liu and L. Chen, “A new compensation method for filtering divergence caused by model errors,” Command Control and Simulation, vol. 34, no. 3, pp. 95–101, 2012.
  • S. Xu, X. Lin, and D. Zhao, “Strong tracking SRCKF and its application in vessel dynamic positioning,” Chinese Journal of Scientific Instrument, vol. 34, no. 6, pp. 1266–1272, 2013.
  • J. Wang, H. Shui, and H. Ma, “Implementation framework of filters in Kalman structure,” Journal of Data Acquisition and Processing, vol. 24, no. 1, pp. 61–66, 2009.
  • X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking–-part V: multiple-model methods,” IEEE Transactions on Aerospace and Electronic Systems, vol. 41, no. 4, pp. 1255–1321, 2005. \endinput