A general class of lower bounds in parameter estimation 论文
1988IEEE Transactions on Information Theory引用 231
Blind Source Separation TechniquesFault Detection and Control SystemsTarget Tracking and Data Fusion in Sensor Networks
摘要
A general class of Bayesian lower bounds on moments of the error in parameter estimation is formulated, and it is shown that the Cramer-Rao, the Bhattacharyya, the Bobrovsky-Zakai, and the Weiss-Weinstein lower bounds are special cases in the class. The bounds can be applied to the estimation of vector parameters and any given function of the parameters. The extension of these bounds to multiple parameter is discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>