Notes on the tightness of the hybrid Cramér-Rao lower bound
IEEE Transactions on Signal Processing
Fundamental limits of wideband localization: part I: a general framework
IEEE Transactions on Information Theory
Fundamental limits of wideband localization: part II: cooperative networks
IEEE Transactions on Information Theory
General classes of performance lower bounds for parameter estimation: part II: Bayesian bounds
IEEE Transactions on Information Theory
On the accuracy of localization systems using wideband antenna arrays
IEEE Transactions on Communications
Barankin-type lower bound on multiple change-point estimation
IEEE Transactions on Signal Processing
Fast communication: Line spectrum estimation with probabilistic priors
Signal Processing
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The Barankin (1949) bound is a realizable lower bound on the mean-square error (MSE) of any unbiased estimator of a (nonrandom) parameter vector. We present a Barankin-type bound which is useful in problems where there is a prior knowledge on some of the parameters to be estimated. That is, the parameter vector is a hybrid vector in the sense that some of its entries are deterministic while other are random variables. We present a simple expression for a positive-definite matrix which provides bounds on the covariance of any unbiased estimator of the nonrandom parameters and an estimator of the random parameters, simultaneously. We show that the Barankin bound for deterministic parameters estimation and the Bobrovsky-Zakai (1976) bound for random parameters estimation are special cases of our proposed bound