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In many areas, such as speech processing, communication, and measurement systems, where the actual signal is not directly available, Wiener deconvolution is a widely used signal restoration technique. The accuracy of the estimated signal in Wiener deconvolution is determined by the unknown filter parameter. To compute this unknown parameter, complex and time-consuming numerical as well as iterative computation approaches were proposed in the literature, which also often compute an unoptimized unknown filter parameter. To address the computational problem and estimate a more accurate unknown filter parameter, the authors reported an analytical solution in a prior work for a given signal to noise ratio. However, it was revealed that the actual optimum parameter value also varies with noise also. In this paper, the authors propose a new analytical solution for optimum parameter estimation that includes noise as well. The authors compared the modified analytical solution to existing numerical computation methods for different values of Signal to Noise Ratio and demonstrated that the proposed methods’ optimum parameter estimation is always close to the actual optimum value, which resulted in more accurate signal deconvolution. Proposed mathematical solutions are also verified with experimental signals, acquired in a laboratory. [ABSTRACT FROM AUTHOR]

All real-time signals observed from various measurement systems require signal-processing techniques like the deconvolution, to compensate for the effect of transfer function of the systems. Wiener deconvolution is a widely used signal-processing technique for signal restoration. Often, if the power spectral density of signal and noise is unknown, the accuracy of the restored signal depends on an unknown filter parameter. In the literature, time-consuming, iterative, computational methods were reported to estimate the parameter. However, many of these methods give a range of optimum values instead of an unique value and often lead to either over-smoothing or under-smoothing. In this paper, novel analytical expressions are presented through which the unknown parameter can be estimated explicitly. The analytical results of this study are compared with the numerical methods, and they are found to be accurate and robust against numerical evaluation. Further, the results are demonstrated for signals obtained experimentally from the pulsed electroacoustic system and a network, in authors’ laboratory. [ABSTRACT FROM PUBLISHER]