Target Tracking based on Improved Unscented Particle Filter with Markov Chain Monte Carlo

REFERENCES

• S. Arulampalam , S. Maskell , N. Gordon , and T. Clapp , “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process ., Vol. 50, no. 2, pp. 174–88, Feb. 2002.
   Web of Science ®Google Scholar
• B. Ristic , S. Arulampalam , and N. Gordon , In Beyond Kalman Filter: Particle Filters Tracking Applications , 1st ed. London: Artech House , 2004.
   Google Scholar
• J. Mu and Y. Licai , “Cubature particle filter with MCMC and applications to re-entry ballistic target tracking,” IACSIT Int. J. Eng. Technol. , Vol. 8, no. 1, pp. 65–9, Feb. 2016.
   Google Scholar
• S. Z. Bi and X. Y. Ren , “Maneuvering target Doppler-bearing tracking with signal time delay using interacting multiple model algorithms,” Prog. Electromagnet. Res. , Vol. 87, pp. 15–41, 2008.
   Web of Science ®Google Scholar
• Z. Tang , C. Sun , and Z. Liu , “The tracking algorithm for maneuvering target based on adaptive Kalman filter,” Int. Arab J. Inform. Technol. , Vol. 10, no. 5, pp. 453–9, Sep. 2013.
   Web of Science ®Google Scholar
• E. Daeipour and Y. Bar-Shalom , “An interacting multiple model approach for target tracking with glint noise,” IEEE Trans. Aerospace Electron. Syst. , Vol. 31, no. 2, pp. 76–715, 1995.
   Web of Science ®Google Scholar
• X. Li and V. Jilkov , Survey of maneuvering target tracking. Part V: Multiple-model methods,” IEEE Trans. AES , Vol. 41, no. 4, pp. 1255–321, Oct. 2005.
   Google Scholar
• S. Julier , J. Uhlmann , and H. F. Durrant-Whyte , “A new method for the nonlinear transformation of means and covariances in filters and estimators,” IEEE Trans. Automat. Control , Vol. 45, no. 3, pp. 477–82, Mar. 2000.
   Web of Science ®Google Scholar
• S. Koteswara Raoa , K. Raja Rajeswari , and K. S. Lingamurty , “Unscented Kalman filter with application to bearings-only target tracking,” IETE J. Res. , Vol. 55, no. 2, pp. 63–7, 2009.
   Web of Science ®Google Scholar
• R. Kumar Jatoth and T. Kishore Kumar , “Hybrid GA-PSO based tuning of unscented Kalman filter for bearings only tracking,” Int. J. Inform. Comput. Technol. , Vol. 4, no. 3, pp. 315–28, 2014.
   Google Scholar
• R. Merwe , “The unscented particle filter,” in Proceeding of Advances in Neural Information Processing Systems , Denver, CO , Nov. 2000.
   Google Scholar
• R. Merwe , A. Doucet , N. Freitas , and E. Wan , “The unscented particle filter,” Engineering Department, Cambridge University, Technical Report CUED/F INFENG/TR 380, 2000.
   Google Scholar
• Q. C. Wang , J. Li , M. X. Zhang , and C. H. Yang , “H-infinity filter based particle filter for maneuvering target tracking,” Progress Electromagnet. Res. B , Vol. 30, pp. 103–16, 2011.
   Google Scholar
• R. Karlsson and N. Bergman , “Auxiliary particle filters for tracking a maneuvering target,” in Proceeding of the 39th IEEE Conference on Decision and Control , Vol. 4, Sydney, NSW, Australia, 2000, pp. 3891–5. IEEE.
   Google Scholar
• M. S. Djogatović , M. J. Stanojević , and N. Mladenović , “A variable neighborhood search particle filter for bearings-only target tracking,” Comput. Oper. Res. , Vol. 52, pp. 192–202, Dec. 2014.
   Web of Science ®Google Scholar
• B. Cao , C. Ma , and Z. Liu , “Particle filter with fine resampling for bearings-only tracking,” Procedia Eng. , Vol. 29, pp. 3685–90, 2012.
   Google Scholar
• J. George and M. Mathurakani , “Bearing only tracking using a particle filter,” Int. J. Eng. Res. Gen. Sci. , Vol. 3, no. 1, pp. 1046–9, Jan.-Feb. 2015.
   Google Scholar
• L. Jing and P. Vadakkepat , “Interacting MCMC particle filter for tracking manoeuvring target,” Digital Signal Process. , Vol. 20, pp. 561–74, 2010.
   Web of Science ®Google Scholar
• S. Chena and W. Huang , “Maneuvering target tracking from nautical radar images using particle-Kalman filters,” J. Electromagnet. Waves Appl. , Vol. 27, no. 18, pp. 2366–78, 2013.
   Web of Science ®Google Scholar
• Z. Ronghui , X. Qin , and W. Jianwei , “Modified unscented particle filter for nonlinear Bayesian tracking,” J. Syst. Eng. Electron. , Vol. 19, no. 1, pp. 7–14, Feb. 2008.
   Web of Science ®Google Scholar
• Q. Ge , W. Li , and C. Wen , “SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking,” J. Zhejiang Univ. Sci. C , Vol. 12, no. 8, pp. 678–86, 2011.
   Web of Science ®Google Scholar
• G. Rong-hua and Q. Zheng , “An unscented particle filter for ground manoeuvring target tracking,” J. Zhejiang Univ. Sci. A , Vol. 8, no. 10, pp. 1588–95, Oct. 2007.
   Web of Science ®Google Scholar
• G. G. Rigatos , “Particle filtering for state estimation in nonlinear industrial systems,” IEEE Trans. Instrum. Meas. , Vol. 58, no. 11, pp. 3885–900, 2009.
   Web of Science ®Google Scholar
• M. Clerc and J. Kennedy , “The particle swarm—explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput. , Vol. 6, no. 1, pp. 58–73, 2002.
   Web of Science ®Google Scholar
• H. Na , X. Dianguo , and L. Huang , “The application of particle swarm optimization to passive and hybrid active power filter design,” IEEE Trans. Ind. Electron. , Vol. 56, no. 8, pp. 2841–51, Aug. 2009.
   Web of Science ®Google Scholar
• C. Andrieu , N. De Freitas , A. Doucet , and M. I. Jordan , “An introduction to MCMC for machine learning,” J. Mach. Learn. , Vol. 50, pp. 5–43, Jan. 2003.
   Web of Science ®Google Scholar
• J. Huang and W. Hu , “MCMC-particle-based group tracking of space objects within Bayesian framework,” Adv. Space Res. , Vol. 53, pp. 280–94, 2014.
   Web of Science ®Google Scholar