Robust ML estimation for unknown numbers of signals: Performance study

We study the performance of a recently proposed robust ML estimation procedure for unknown numbers of signals. This approach finds the ML estimate for the maximum number of signals and selects relevant components associated with the true parameters from the estimated parameter vector. Its computational cost is significantly lower than conventional methods based on information theoretic criteria or multiple hypothesis tests. We show that the covariance matrix of relevant estimates is upper and lower bounded by two covariance matrices. These bounds are easy to compute by existing results for standard ML estimation. Our analysis is further confirmed by numerical experiments over a wide range of SNRs.