Particular Filtering Techniques

  • Thierry Chonavel
Part of the Advanced Textbooks in Control and Signal Processing book series (C&SP)


Purpose In many problems, we want to evaluate a process X from partial knowledge of a process Y. According to whether knowledge about Y at instant n is limited to that of H Y,n , or to that of H Y,o:n = span {Y o,...,Y n }, we may opt to evaluate X n by X n /H Y,n , or X n /H Y,o:n The resolution of these problems is known as Wiener filtering and Kaiman filtering respectively. We next indicate how Kaiman’s recursive filtering can be generalised to computing recursively the distribution of X n conditional to {Y0,... , Y n } for systems that may not be linear. We finish this part with the presentation of the matched filter that enables detection of a known deterministic signal in the presence of noise.


Kalman Filter Recurrence Equation State Space Model Matched Filter Wiener Filter 
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Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Thierry Chonavel
    • 1
  1. 1.ENST de BretagneTechnopôle de Brest IroiseBrest CedexFrance

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