Power Adaptation for Vector Parameter Estimation according to Fisher Information based Optimality Criteria

TL;DR

This paper develops optimal power adaptation strategies for vector parameter estimation using Fisher information criteria, providing closed-form solutions and extensions to complex models, with numerical validation.

Contribution

It introduces new power allocation methods based on Fisher information criteria, including solutions for models with nuisance parameters and nonlinear transformations.

Findings

Closed-form solutions for power allocation problems

Extensions to models with nuisance parameters

Numerical results demonstrating performance improvements

Abstract

The optimal power adaptation problem is investigated for vector parameter estimation according to various Fisher information based optimality criteria. By considering an observation model that involves a linear transformation of the parameter vector and an additive noise component with an arbitrary probability distribution, six different optimal power allocation problems are formulated based on Fisher information based objective functions. Via optimization theoretic approaches, various closed-form solutions are derived for the proposed problems. Also, the results are extended to cases in which nuisance parameters exist in the system model or certain types of nonlinear transformations are applied on the parameter vector. Numerical examples are presented to investigate performance of the proposed power allocation strategies.