Yann Le Gall

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

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.

Bayesian source localization with uncertain Green's function in an uncertain shallow water oceana)

The localization of an acoustic source in the oceanic waveguide is a difficult task because the oceanic environment is often poorly known. Uncertainty in the environment results in uncertainty in the source position and poor localization results. Hence, localization methods dealing with environmental uncertainty are required. In this paper, a Bayesian approach to source localization is introduced in order to improve robustness and obtain quantitative measures of localization uncertainty. The Greens function of the waveguide is considered as an uncertain random variable whose probability density accounts for environmental uncertainty. The uncertain distribution over range and depth is then obtained through the integration of the posterior probability density (PPD) over the Greens function probability density. An efficient integration technique makes the whole localization process computationally efficient. Some results are presented for a simple uncertain Greens function model to show the ability of the proposed method to give reliable PPDs.

Passive estimation of the waveguide invariant per pair of modes

In many oceanic waveguides, acoustic propagation is characterized by a parameter called waveguide invariant. This property is used in many passive and active sonar applications where knowledge of the waveguide invariant value is required. The waveguide invariant is classically considered as scalar but several studies show that it is better modeled by a distribution because of its dependence on frequency and mode pairs. This paper presents a new method for estimating the waveguide invariant distribution. Using the noise radiated by a distant ship and a single hydrophone, the proposed methodology allows estimating the waveguide invariant for each pair of modes in shallow water. Performance is evaluated on simulated data.

Performance Analysis of Single-Receiver Matched-Mode Localization

Acoustic propagation in shallow water at low frequency is characterized by a few propagating modes. When the source is impulsive or short enough, the modes can be extracted from the signal received on a single sensor using a warping operator. This opens the door to single-receiver matched-mode processing (SR-MMP) as a means to estimate source location and/or ocean environmental parameters. While the applicability of SR-MMP has been demonstrated through several experiments, prediction of its achievable performance has not been fully investigated. In this paper, performance analysis of SR-MMP is carried out using numerical simulations of a typical shallow water environment, incorporating possible environmental mismatch as well as degradations resulting from nonideal modal filtering. SR-MMP is a nonlinear estimation problem that presents three regions of operation: the high SNR asymptotic region driven by local errors, the intermediate SNR threshold region driven by sidelobe ambiguities and the low SNR no-information region. The method of interval errors, which gives computationally efficient and reliable mean squared error performance prediction, is used to conduct the analysis. The results suggest that the SR-MMP performance depends strongly on the source/receiver depth. A significant loss in performance is observed when the receiver is located at a node common to two modes. Receiver depth must therefore be chosen with care. SR-MMP seems to be quite robust to mismatch on the seabed properties alone but does not handle well the combined effect of seabed and water column mismatches. Nonideal modal filtering has a moderate impact on performance.

Matched-Field Performance Prediction with Model Mismatch

Matched-field estimation is known to be sensitive to mismatch between the assumed replica of the acoustic field and the actual field. An interval error-based method (MIE) is proposed to predict the mean-squared error (MSE) performance for multisnapshot and multifrequency maximum-likelihood matched-field estimation under model mismatch. The source signal is assumed deterministic unknown. Global errors are predicted by deriving exact expressions of pairwise error probabilities with model mismatch in conjunction with the use of the Union bound. Local errors are approximated using a Taylor expansion of the MSE. Numerical examples show the accuracy of the method.

Separation of moving ship striation patterns using physics-based filtering

When a ship is moving toward an acoustic receiver in an oceanic waveguide, the time-frequency representation of the recorded signal exhibits a striation pattern that can be useful in numerous applications such as ship localization or geoacoustic inversion. If there are many ships, the striation patterns add up and they must be separated if one wants to study them separately. In this paper, a physics-based filtering scheme for passive underwater acoustics has been developed. The algorithm allows separating the time-frequency striations of two different moving ships. The proposed method considers filtering the 2D Fourier transform of the received spectrogram. The filter design is based on the waveguide invariant principle and on some a priori knowledge on the oceanic waveguide. The noise nature on the spectrogram is taken into account by introducing a nonlinearity to the filtering scheme. The algorithm thus corresponds to a nonlinear homomorphic filter. The method is validated on both simulated data and exp...