A modified lattice algorithm for deconvolving filtered impulsive processes

Many least-squares deconvolution algorithms are based on fitting an autoregressive model to the observed data. An implicit assumption in these algorithms is that the data was generated by a zero mean white noise process driving a linear filter. In many applications such as speech analysis or seismic deconvolution this assumption does not hold, and the performance of conventional deconvolution algorithms is, therefore, degraded. This paper presents an approach that attempts to alleviate this problem. The proposed technique combines a joint-process lattice filter with a nonlinear estimator of the driving process.