Transient multiexponential data analysis using a combination of ARMA and ECD methods - Jibia, Abdussamad Umar; Salami, Momoh Jimoh Eyiomika (IACSIT Press, Singapore, 2012)
Another attempt at estimating the time constants and number of components of multiple exponentials in white Gaussian noise is presented. Based on classical Gardner transform, the approach consists of two techniques. First, exponential compensation deconvolution method is used to deconvolved the discrete convolution model arising from the application of Gardner transform. The deconvolved data is then truncated and further processed using autoregressive moving average (ARMA) model whose AR parameters are determined by using high-order Yule-Walker equations via the singular value decomposition (SVD) algorithm. Simulations carried out using a number of synthetic signals demonstrate the effectiveness of the proposed technique. Simulation results shows that this combination is more effective than many existing techniques. It is clearly demonstrated that the proposed approach supersedes a number of popular techniques. Its limitations are also highlighted.