A Direct Method to Generate Approximations of the Barankin Bound
Angela Quinlan, Éric Chaumette, P. Larzabal · 2006
The search for an easily computable but tight approximation of the Barankin bound (BB) is important for the prediction of the signal-to-noise ratio (SNR) value where the Cramer-Rao bound (CRB) becomes unreliable for prediction of maximum likelihood estimators (MLE) variance. In this paper we propose a method for the derivation of a general class of BB approximations which has the advantage of a clear interpretation. This method suggests a new practical BB approximation, whose computational complexity does not exceed that of the CRB but which seems tighter than existing approximations