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J. L. Maryak, J. C. Spall and B. D. Heydon, “Use of the Kalman Filter for Interference in State-Space Models with Unknown Noise Distributions,” IEEE Transactions on Automatic Control, Vol. 49, No. 1, 2005, pp. 87-90.

has been cited by the following article:

• TITLE: Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

  AUTHORS: Juuso T. Olkkonen, Hannu Olkkonen

  KEYWORDS: State-Space Modelling, Dynamic System Analysis, EEG

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.4, November 3, 2011

  ABSTRACT: This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.