Sequential joint signal detection and signal-to-noise ratio estimation

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) typ...

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Bibliographic Details
Published in2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4606 - 4610
Main Authors Fauss, M., Nagananda, K. G., Zoubir, A. M., Poor, H. V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2017
Subjects
Bayes methods
Bernoulli transformation
Error probability
Estimation
joint detection and estimation
Optimization
Random variables
Sequential analysis
Signal detection
Signal to noise ratio
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Summary:The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance. This optimization problem is reduced to a more tractable formulation by transforming the observed signal and noise sequences to a single sequence of Bernoulli random variables; joint detection and estimation is then performed on the Bernoulli sequence. This transformation renders the problem easily solvable, and results in a computationally simpler sufficient statistic compared to the one based on the (untransformed) observation sequences. Experimental results demonstrate the advantages of the proposed method, making it feasible for applications having strict constraints on data storage and computation.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7953029