Using the EM algorithm to increase the number of signals estimable by 2-D parametric techniques

This paper provides an easy method for increasing the number of modes estimable by 2-D parameter estimation schemes which use a single snapshot of the data. For data of size M₁×M₂ few of these techniques allow the number of modes to exceed M₁ and/or M₂. As these restrictions are not inherent to the model but the algorithms themselves, this problem is circumvented by treating the observed data as an incomplete version of a larger data set. An existing 2-D parameter estimation scheme is then used for the maximization (M) step of the expectation-maximization (EM) algorithm. In this way, one can increase the number of modes the scheme can estimate without changing the scheme itself