A subband algorithm for estimating the parameters of two-dimensional exponential signals

We propose an adaptive subband decomposition scheme designed to estimate the parameters of two-dimensional (2D) exponential signals from large data sets. The principle of the method consists to perform recursive 2D decimation and estimation steps. At each resulting subband, a stopping rule is evaluated to decide whether the decomposition should be continued or stopped. The proposed rule is based on the Fisher g-test applied on the estimation error. The advantages and limitations of the subband approach are discussed through a simulation example. The method is then demonstrated on an experimental 2D nuclear magnetic resonance spectroscopy signal.