Robust burg estimation of radar scatter matrix for constrained stationary SIRV

We propose estimators of the scatter matrix of a scale mixture of Gaussian stationary autoregressive vectors. For Gaussian autoregressive processes, Burg methods are often used in case of stationarity for their efficiency when few samples are available. Unfortunately, if we directly apply these methods to estimate the common scatter matrix of N vectors coming from a non-Gaussian distribution, the efficiency will strongly decrease. We propose then to adapt these methods to scale mixtures of Gaussian vectors by changing the energy functional minimized in the Burg algorithm. Moreover, we propose robust versions of our estimators in order to be not sensitive to a contamination of the sample.