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Bayesian Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation | IEEE Conference Publication | IEEE Xplore

Bayesian Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation


Abstract:

Jointly detecting a signal in noise and, in case a signal is present, estimating the Signal-to-Noise Ratio (SNR) is investigated in a sequential setup. The sequential tes...Show More

Abstract:

Jointly detecting a signal in noise and, in case a signal is present, estimating the Signal-to-Noise Ratio (SNR) is investigated in a sequential setup. The sequential test is designed such that it achieves desired error probabilities and Mean-Squared Errors (MSEs), while the expected number of samples is minimized. This problem is first converted to an unconstrained problem, which is then reduced to an optimal stopping problem. The solution, which is obtained by means of dynamic programming, is characterized by a non-linear Bellman equation. A gradient ascent approach is then presented to select the cost coefficients of the Bellman equation such that the desired error probabilities and MSEs are achieved. A numerical example concludes the work.
Date of Conference: 02-06 September 2019
Date Added to IEEE Xplore: 18 November 2019
ISBN Information:

ISSN Information:

Conference Location: A Coruna, Spain

I. Introduction

Sequential analysis is a field of statistics initially introduced by Abraham Wald in the late 1940s [1]. The aim of sequential analysis is to perform statistical inference, e.g., estimation or detection, with a minimum number of samples while ensuring a certain inference quality. An overview on sequential detection methods is given in [2] and on sequential estimation methods in [3]. Sequential inference is an area of ongoing research. Especially for low power or time critical applications, sequential methods are preferable to conventional ones.

References

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