Application of Minimum Distortion Filtering to Identification of Linear Systems Having Non-uniform Sampling Period

  • Graham C. Goodwin
  • Mauricio G. Cea


We consider the problem of identification of continuous time systems when the data is collected using non-uniform sampling periods. We formulate this problem in the context of Nonlinear Filtering. We show how a new class of nonlinear filtering algorithm (Minimum Distortion Filtering) can be applied to this problem. A simple example is used to illustrate the performance of the algorithm. We also compare the results with those obtained from (a particular realization) of Particle Filtering.

The chapter is inspired by the work of Peter Young who has made a life time of contributions to parameter estimation for real world systems.


Kalman Filter Particle Filter Vector Quantization Voronoi Cell Particle Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of NewcastleUniversity DriveAustralia

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