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Simulation-based methods for blind maximum-likelihood filter identification
1999
Signal Processing
Blind linear system identification consists in estimating the parameters of a linear time-invariant system given its (possibly noisy) response to an unobserved input signal. Blind system identification is a crucial problem in many applications which range from geophysics to telecommunications, either for its own sake or as a preliminary step towards blind deconvolution (i.e. recovery of the unknown input signal). This paper presents a survey of recent stochastic algorithms, related to the
doi:10.1016/s0165-1684(98)00182-0
fatcat:odgii7yg3jckbb7smvfh4m4kem