New Cramer-Rao-Type Bound for Constrained Parameter Estimation

نویسندگان

  • Eyal Nitzan
  • Tirza Routtenberg
  • Joseph Tabrikian
چکیده

Non-Bayesian parameter estimation under parametric constraints is encountered in numerous applications in signal processing, communications, and control. Mean-squared-error (MSE) lower bounds are widely used as performance benchmarks and for system design. The well-known constrained Cramér-Rao bound (CCRB) is a lower bound on the MSE of estimators that satisfy some unbiasedness conditions. In many constrained estimation problems, these unbiasedness conditions are too strict and popular estimators, such as the constrained maximum likelihood estimator, do not satisfy them. In addition, MSE performance can be uniformly improved by implementing estimators that do not satisfy these conditions. As a result, the CCRB is not a valid bound on the MSE of such estimators. In this paper, we propose a new definition for unbiasedness in constrained settings, denoted by C-unbiasedness, which is based on using Lehmann-unbiasedness with a weighted MSE (WMSE) risk and taking into account the parametric constraints. In addition, a Cramér-Rao-type bound on the WMSE of C-unbiased estimators, denoted as Lehmann-unbiased CCRB (LU-CCRB), is derived. It is shown that in general, C-unbiasedness is less restrictive than the CCRB unbiasedness conditions. Thus, the LU-CCRB is valid for a larger set of estimators than the CCRB and C-unbiased estimators with lower WMSE than the corresponding CCRB may exist. In the simulations, we examine linear and nonlinear estimation problems under nonlinear parametric constraints in which the constrained maximum likelihood estimator is shown to be C-unbiased and the LU-CCRB is an informative bound on its WMSE, while the corresponding CCRB is not valid. Index Terms Non-Bayesian parameter estimation, mean-squared-error (MSE), parametric constraints, constrained Cramér-Rao bound (CCRB), Lehmann-unbiasedness This research was partially supported by THE ISRAEL SCIENCE FOUNDATION (grant No. 1160/15 and grant No. 1173/16). E. Nitzan, T. Routtenberg, and J. Tabrikian are with the Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva 84105, Israel, e-mail: [email protected], [email protected], [email protected]. 2

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عنوان ژورنال:
  • CoRR

دوره abs/1802.02384  شماره 

صفحات  -

تاریخ انتشار 2018