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Matched-Field Processing Performance Under the Stochastic and Deterministic Signal Models

Yann Le Gall 1, 2, 3 François-Xavier Socheleau 3, 4 Julien Bonnel 1, 2
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Pôle STIC_AP
ENSTA Bretagne - École Nationale Supérieure de Techniques Avancées Bretagne
4 Lab-STICC_TB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Matched-field processing (MFP) is commonly used in underwater acoustics to estimate source position and/or oceanic environmental parameters. Performance prediction of the multisnapshot and multifrequency MFP problem is of critical importance. To this end, two signal models are usually considered: the stochastic model, which assumes that the source signal is a stochastic process, and the deterministic model, which assumes that the source signal is a deterministic quantity. The Ziv-Zakai bound (ZZB) and the method of interval errors (MIE), which both rely on the computation of a so-called pairwise error probability, proved to be useful tools for MFP performance prediction. However, only the stochastic model has been considered so far. This paper provides a method that allows to compute the pairwise error probability, hence to use the ZZB and MIE, under both the stochastic and deterministic signal models. The proposed approach, based on recent results on quadratic forms in Gaussian variables, unifies the two models under the same formalism. The results are illustrated through the computation of the ZZB and MIE performance analysis. The Bayesian and the hybrid Cramèr-Rao bounds are also given for comparison.
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Article dans une revue
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https://hal.archives-ouvertes.fr/hal-01089761
Contributeur : Annick Billon-Coat <>
Soumis le : mardi 2 décembre 2014 - 12:29:56
Dernière modification le : mercredi 6 janvier 2021 - 15:36:35

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Yann Le Gall, François-Xavier Socheleau, Julien Bonnel. Matched-Field Processing Performance Under the Stochastic and Deterministic Signal Models. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, 62 (22), pp.5825 - 5838. ⟨10.1109/TSP.2014.2360818⟩. ⟨hal-01089761⟩

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