Multiparameter optimization of inverse filtering algorithms

In this paper inverse filtering of transient signals is dealt width. The problem is ill-conditioned, which means that small uncertainty in the measurement causes large deviation in the reconstructed signal. This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. The deconvolution algorithms are usually controlled by one or few number of parameters. Several algorithms can be found in the literature to find the best setting of inverse filtering methods, however, usually methods with only one free parameter are handled. An algorithm is proposed, based on a spectral model, to optimize several parameters. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, noise and signal to be measured. The superiority of the proposed optimization method is demonstrated both on simulated and experimental data