Matched-Field Processing Performance Under the Stochastic and Deterministic Signal Models - ENSTA Bretagne - École nationale supérieure de techniques avancées Bretagne Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2014

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

Résumé

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.
Fichier non déposé

Dates et versions

hal-01089761 , version 1 (02-12-2014)

Identifiants

Citer

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

Altmetric

Partager

Gmail Facebook X LinkedIn More