Understanding the method of interval errors from the information theory perspective

Meng, De Jun; Xu, Wen; Xia, Menglu

doi:10.1109/icassp.2009.4960034

Cited by 1 publication

(2 citation statements)

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“…The choice of is based on the observed sidelobe structure of the noise free ML criterion. It should be noted that the results were found to be little sensitive to the choice of in the asymptotic and threshold regions ( or lead to similar results) which may be explained by the results in [37]. Furthermore, the Cramèr-Rao bounds are computed using the results presented in Appendix A.…”

Section: A Computational Issues 1) Evaluation Of the Pairwise Error mentioning

confidence: 71%

“…Denote also the SNR at the receiver for the frequency when the true parameter is (35) The eigenvalues , and the scalar are expressed as follows (36) (37) Therefore, the computation of the exact SML pairwise error probability only requires the calculation of simple quantities and the numerical evaluation of a one-dimensional integral.…”

Section: B Stochastic Signal Modelmentioning

confidence: 99%

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

Gall

Socheleau

Bonnel

2014

IEEE Trans. Signal Process.

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International audienceMatched-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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Section: A Computational Issues 1) Evaluation Of the Pairwise Error mentioning

confidence: 71%

Section: B Stochastic Signal Modelmentioning

confidence: 99%

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

Gall

Socheleau

Bonnel

2014

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

Understanding the method of interval errors from the information theory perspective

Cited by 1 publication

References 7 publications

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

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

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