Estimation of the state of a nonlinear process in the presence of nongaussian noise and disturbances

Bernard Friedland, Irwin Bernstein

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The optimum estimate is defined as one which maximizes the conditional probability density function of the sequence of states Xn = {x0, ..., xn} in a discrete-time dynamic process, given a sequence of observations Yn = {y1, ..., yn}. The equations governing this estimate are derived for nonlinear processes in the presence of nongaussian noise and disturbances. A recursive technique is given for computing an approximation x̄n to the optimum estimate x̄n given xn-1 and yn. This technique reduces to the Kalman-Busy algorithm for linear processes with gaussian noise and disturbances. An algorithm for correcting the approximate estimate is also derived. The discrete-time results are formally extended to continuous-time processes.

Original languageEnglish (US)
Pages (from-to)455-480
Number of pages26
JournalJournal of the Franklin Institute
Volume281
Issue number6
StatePublished - Jun 1 1966
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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