We use the term hybrid parameter vector to refer to a vector which consists of both random and non-random parameters. In this paper we present a novel Barankin-type lower bound which bounds the estimation error of a hybrid parameter vector. The bound is expressed in a simple matrix form which consists of a non-Bayesian bound on the non-random parameters, a Bayesian bound on the random parameters, and the cross terms. We show that the non-Bayesian Barankin bound for deterministic parameters estimation and the Bobrovsky-Zakai Bayesian bound for random parameters estimation are special cases of the new bound. Also, the multi-parameter Cramer-Rao bound, in its Bayesian or non-Bayesian versions, are shown to be special cases of the new bound.
|Number of pages||3|
|Journal||Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing|
|State||Published - 1996|
|Event||Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA|
Duration: 7 May 1996 → 10 May 1996