On Unbiased Parameter Estimation and Signal Reconstruction

TL;DR

This paper extends the theory of unbiased source localization to parameter estimation and signal reconstruction, providing bounds, probabilistic measures, and insights into noise robustness, supported by numerical experiments.

Contribution

It introduces a theoretical framework for unbiased parameter estimation and signal reconstruction for multiple parameters, including bounds and noise robustness analysis.

Findings

Derived upper bounds on recoverable parameters in noiseless case

Defined a probability measure for successful parameter recovery

Numerical experiments validate theoretical results

Abstract

In this paper, we expand the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction of an arbitrary number of non-zero parameters to be recovered. The topic touches on the concept of exact reconstructibility, most commonly known in compressed sensing and multisource estimation in various imaging problems. The theoretical results derive upper bounds on the number of recoverable parameters in the noiseless case, and a probability measure is defined to assess the probability of obtaining all non-zero parameters with correct magnitude order. The work provides a mathematical explanation of the open question regarding the noise robustness of standardized and unbiased methods. Also, the paper reveals a trade-off between the number of sensors and the signal-to-noise ratio. Numerical experiments demonstrate the theoretical findings.