In this paper inverse filtering of transient signals is dealtwidth. The problem is ill-conditioned, which means that smalluncertainty in the measurement causes large deviation in thereconstructed signal. This amplified noise has to be suppressed at theprice of bias in the estimation. The most difficult task is to find theoptimal degree of noise reduction. The deconvolution algorithms areusually controlled by one or few number of parameters. Severalalgorithms can be found in the literature to find the best setting ofinverse filtering methods, however, usually methods with only one freeparameter are handled. An algorithm is proposed, based on a spectralmodel, to optimize several parameters. Multiparameter inverse filteringmethods have the advantage that they can be better adapted to themeasurement system, noise and signal to be measured. The superiority ofthe proposed optimization method is demonstrated both on simulated andexperimental data
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