Spectrum Estimation using New Efficient 2-D AR Lattice Modeling of Random Fields

In this paper, the new efficient 2-D autoregressive (AR) lattice modeling technique of random fields is applied to predict the reflection parameters of the 2-D AR data field and the spectrum estimation is carried out. In addition, the AR coefficients of the data field generated as the sum of 2-D complex sinusoids corrupted by additive complex Gaussian noise are predicted and its spectrum is estimated. The results are compared with the classical 2-D FFT-based estimates. The new efficient 2-D (AR) lattice modeling technique is newly presented and based on employing the auxiliary vertical and horizontal prediction error fields. The aim of this method is to obtain simultaneously all possible types of causal and noncausal 2-D models for an arbitrary rectangular shape of the prediction support region. The obtained causal quarter-plain models are mostly found stable.