Optimal Simultaneous Detection and Signal and Noise Power Estimation

TL;DR

This paper develops a jointly optimal detector and estimators for signal detection and SNR estimation under Gaussian noise, addressing a gap in existing frameworks and demonstrating superior performance through simulations.

Contribution

It introduces a new joint detection and estimation framework tailored for Gaussian models, bridging a gap in existing methods.

Findings

Superior performance over separate detection and estimation methods

Effective joint detection and SNR estimation for Gaussian signals

Validated through simulation results

Abstract

Simultaneous detection and estimation is important in many engineering applications. In particular, there are many applications where it is important to perform signal detection and Signal-to-Noise-Ratio (SNR) estimation jointly. Application of existing frameworks in the literature that handle simultaneous detection and estimation is not straightforward for this class of application. This paper therefore aims at bridging the gap between an existing framework, specifically the work by Middleton et al., and the mentioned application class by presenting a jointly optimal detector and signal and noise power estimators. The detector and estimators are given for the Gaussian observation model with appropriate conjugate priors on the signal and noise power. Simulation results affirm the superior performance of the optimal solution compared to the separate detection and estimation approaches.