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

作者：Le Gall Yann^*; Socheleau Francois Xavier; Bonnel Julien

来源：IEEE Transactions on Signal Processing, 2014, 62(22): 5825-5838.

DOI：10.1109/TSP.2014.2360818

• Cramer-Rao bound
• matched-field processing
• maximum likelihood
• method of interval errors
• performance analysis
• underwater acoustics
• Ziv-Zakai bound

Matched-field processing (MFP) is commonly used in underwater acoustics to estimate source position and/or oceanic environmental parameters. Performance prediction of the multi-snapshot 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 Cramer-Rao bounds are also given for comparison.

• 出版日期2014-11-15