Sigma-Point Kalman Filters

Sigma-Point Kalman Filters (Unscented Kalman Filter, Central Difference Kalman Filter, etc.) represents a major advance over traditional Extended Kalman Filtering. Our research focus is on the development of the SPKF for machine learning, including state-estimation, parameter estimation, and dual estimation frameworks.

On-Line Presentations:

• The Unscented Kalman Filter for Nonlinear Estimation (modified from a poster given at Learning Workshop, Snowbird Utah, 2000)
  
  

We have developed a Matlab toolkit called ReBEL that contains the above mentioned and other related algorithms. See the ReBEL homepage for more detail.

• The Unscented Particle Filter, Tech report CUED/F-INFENG/TR-380, Cambridge University Engineering Department, Cambridge, England, Aug, 2000, pdf, postscript.
  
  
• The Unscented Kalman Filter for Nonlinear Estimation, in Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control (AS-SPCC), IEEE, Lake Louise, Alberta, Canada, Oct, 2000, postscript, html.
  
  
• Dual Estimation and the Unscented Transformation, in Advances in Neural Information Processing Systems 12, pp. 666-672, MIT Press, Eds. S.A. Solla and T.K. Leen and K.-R. Muller, Nov, 2000, pdf, postscript.
  
  
• The Unscented Kalman Filter, Kalman Filtering and Neural Networks, pp. 221-280., Wiley Publishing, 2001, Edited by Simon Haykin.
  
  
• Efficient Derivative-Free Kalman Filters for Online Learning. Submitted to European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium. April 25-26-27, 2001, postscript.
  
  
• The Square-Root Unscented Kalman Filter for State and Parameter-Estimation. Submitted to International Conference on Acoustics, Speech, and SignalProcessing 2001, Salt Lake City, Utah, May, 2001, postscript.
  
  
• R. van der Merwe and E. A. Wan, "Efficient Derivative-Free Kalman Filters for Online Learning", in European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr, 2001, pdf, postscript.
  
  
• R. van der Merwe and E. A. Wan, "The Square-Root Unscented Kalman Filter for State and Parameter-Estimation", in International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, Utah, May, 2001, pdf, DjVu, postscript.
  
  
• E. A. Wan and R. van der Merwe, "Kalman Filtering and Neural Networks", chap. Chapter 7 : The Unscented Kalman Filter, (50 pages), Wiley Publishing, Eds. S. Haykin, 2001, pdf, postscript.
  
  
• R. van der Merwe, N. de Freitas, A. Doucet and E. Wan, "The Unscented Particle Filter", num. CUED/F-INFENG/TR 380, Cambridge University Engineering Department, Cambridge, England, Aug, 2000, pdf, postscript.
  
  
• R. van der Merwe, A. Doucet, N. de Freitas and E. Wan, "The Unscented Particle Filter", in Advances in Neural Information Processing Systems (NIPS13), MIT Press, Eds. T. K. Leen, T. G. Dietterich and V. Tresp, Dec, 2000, pdf, postscript.
  
  
• Eric A. Wan and Rudolph van der Merwe, "The Unscented Kalman Filter for Nonlinear Estimation", in Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control (AS-SPCC), IEEE, Lake Louise, Alberta, Canada, Oct, 2000, postscript.
  
  
• Eric A. Wan and Rudolph van der Merwe and Alex T. Nelson, "Dual Estimation and the Unscented Transformation", in Advances in Neural Information Processing Systems 12, pp. 666-672, MIT Press, Eds. S.A. Solla and T.K. Leen and K.-R. Muller, Nov, 2000, rvdmerwe@ece.ogi.edu, pdf, postscript.