"Sound Source Localization with Data and Model Uncertainties Using the EM and Evidential EM Algorithms" (Post-doc 2015 - 2016)
Publications scientifiques au M2P2
2019
Shahram Khazaie, Xun Wang, Dimitri Komatitsch, Pierre Sagaut. Uncertainty quantification for acoustic wave propagation in a shallow water environment. Wave Motion, Elsevier, 2019, 91, pp.102390. ⟨10.1016/j.wavemoti.2019.102390⟩. ⟨hal-02467993⟩ Plus de détails...
Sound wave propagation in a shallow water environment is complex due to e.g. the uncertainties of sound speed profile being inhomogeneous and imprecisely measured, the bottom reflections, etc. The propagation and influence of several uncertainty parameters are quantified in this paper. A four-layer model, which can approximately represent a wide range of shallow water environments, is considered; six parameters representing sound speed profile and water depth are considered as random variables. We investigate how the wave field (pressure) in this model is influenced by these uncertainties. For this purpose, the sound field is computed for different realizations of the random variables, when the medium is excited with sources whose frequencies are appropriate, for example, for marine seismic exploration applications. Since classical Monte Carlo methods require a huge sample size to converge, we use three surrogate modeling techniques (Kriging, Polynomial Chaos, and Polynomial Chaos-based Kriging). The proposed methods require much smaller sample sizes, which makes the uncertainty quantification (UQ) possible. Wavelength-to-depth ratio (lambda/d) is introduced as the key parameter that defines the degree of interaction (reflection and transmission) of the sound waves with the boundaries of the shallow water waveguide. The results show that for small and large values of lambda/d, the wave field is more sensitive to the variations of the water depth and the velocity of the bottom layer, respectively. The robustness (precision) of the surrogate models is shown to decrease for lower values of lambda/d. The proposed UQ methodology can be used for more complicated underwater environments; it is even more advantageous because it can efficiently deal with a large number of model uncertainty parameters and identify the most influential ones.
Shahram Khazaie, Xun Wang, Dimitri Komatitsch, Pierre Sagaut. Uncertainty quantification for acoustic wave propagation in a shallow water environment. Wave Motion, Elsevier, 2019, 91, pp.102390. ⟨10.1016/j.wavemoti.2019.102390⟩. ⟨hal-02467993⟩
Xue Chen, Xun Wang, Paul G. Chen, Qiusheng Liu. Determination of Diffusion Coefficient in Droplet Evaporation Experiment Using Response Surface Method. Microgravity Science and Technology, Springer, 2018, 30, pp.675-682. ⟨10.1007/s12217-018-9645-2⟩. ⟨hal-02112826⟩ Plus de détails...
Evaporation of a liquid droplet resting on a heated substrate is a complex free-surface advection-diffusion problem, in which the main driving force of the evaporation is the vapor concentration gradient across the droplet surface. Given the uncertainty associated with the diffusion coefficient of the vapor in the atmosphere during space evaporation experiments due to the environmental conditions, a simple and accurate determination of its value is of paramount importance for a better understanding of the evaporation process. Here we present a novel approach combining numerical simulations and experimental results to address this issue. Specifically, we construct a continuous function of output using a Kriging-based response surface method, which allows to use the numerical results as a black-box with a limited number of inputs and outputs. Relevant values of the diffusion coefficient can then be determined by solving an inverse problem which is based on accessible experimental data and the proposed response surface. In addition, on the basis of our numerical simulation results, we revisit a widely used formula for the prediction of the evaporation rate in the literature and propose a refined expression for the droplets evaporating on a heated substrate.
Xue Chen, Xun Wang, Paul G. Chen, Qiusheng Liu. Determination of Diffusion Coefficient in Droplet Evaporation Experiment Using Response Surface Method. Microgravity Science and Technology, Springer, 2018, 30, pp.675-682. ⟨10.1007/s12217-018-9645-2⟩. ⟨hal-02112826⟩
Xun Wang, Shahram Khazaie, Dimitri Komatitsch, Pierre Sagaut. Sound-Source Localization in Range-Dependent Shallow-Water Environments Using a Four-Layer Model. IEEE Journal of Oceanic Engineering, Institute of Electrical and Electronics Engineers, 2017, pp.1 - 9. ⟨10.1109/JOE.2017.2775978⟩. ⟨hal-01702364⟩ Plus de détails...
Sound-source localization in shallow water is a difficult task due to the complicated environment, e.g., complex sound-speed profile and irregular water bottom reflections. Full-wave numerical techniques are currently able to accurately simulate the propagation of sound waves in such complex environments. However, the source localization problem, which generally involves a large number of sound propagation calculations, still requires a fast computation of the wave equation, and thus a simplified model is well advised. In this paper, a four-layer model is considered, which is able to approximate a wide range of shallow-water environments, particularly those in summer conditions. More specifically, the medium is assumed to be horizontally stratified and vertically divided into four layers, and the sound speed in each layer is assumed to be constant or varying linearly. Under this assumption, the wave propagation can be rapidly computed via a classical wave number integration method. The main contribution of this paper is to show the suitability of the four-layer model in terms of source localization in a complex (range-dependent) environment. The sound-speed profile is assumed to be vertically irregular and horizontally slowly varying and the bottom is nonflat. In the forward problem, sound propagation in complex underwater environments is simulated via a time-domain full-wave simulation approach called the spectral-element method. The source localization error due to model imprecision is analyzed.
Xun Wang, Shahram Khazaie, Dimitri Komatitsch, Pierre Sagaut. Sound-Source Localization in Range-Dependent Shallow-Water Environments Using a Four-Layer Model. IEEE Journal of Oceanic Engineering, Institute of Electrical and Electronics Engineers, 2017, pp.1 - 9. ⟨10.1109/JOE.2017.2775978⟩. ⟨hal-01702364⟩
Xue Chen, Xun Wang, Paul G. Chen, Qiusheng Liu. Thermal effects of substrate on Marangoni flow in droplet evaporation: Response surface and sensitivity analysis. International Journal of Heat and Mass Transfer, Elsevier, 2017, 113, pp.354 - 365. ⟨10.1016/j.ijheatmasstransfer.2017.05.076⟩. ⟨hal-01532757⟩ Plus de détails...
In this paper, the evaporation of sessile droplets resting on a substrate with different thermal properties is numerically investigated. Computations are based on a transient axisymmetric numerical model. Special attention is paid to evaluate thermal effects of substrate on the structure of bulk fluid flow in the course of evaporation. Numerical results reveal that Marangoni convection induced by non-uniform distribution of temperature along the interface exhibits three distinctly different behaviours: inward flow, multicellular flow and outward flow, consequently resulting in different particle depositions. It is highlighted that three factors (i.e. relative thermal conductivity, relative substrate thickness and relative substrate temperature) strongly affect the flow pattern. In order to further investigate the coupling effects of different influential factors, a Kriging-based response surface method is introduced. We model the flow behaviour as a function of continuous influential factors using a limited number of computations corresponding to discrete values of the inputs. The sensitivities of the Marangoni flow are also analysed using Sobol’ index to study the coupling mechanisms of influential factors. The proposed method can be used to forecast the flow patterns for any input parameter without additional sophisticated computer simulation, and allows to confidently estimate an unknown environmental parameter.
Xue Chen, Xun Wang, Paul G. Chen, Qiusheng Liu. Thermal effects of substrate on Marangoni flow in droplet evaporation: Response surface and sensitivity analysis. International Journal of Heat and Mass Transfer, Elsevier, 2017, 113, pp.354 - 365. ⟨10.1016/j.ijheatmasstransfer.2017.05.076⟩. ⟨hal-01532757⟩
Journal: International Journal of Heat and Mass Transfer
Xun Wang, Shahram Khazaie, Luca Margheri, Pierre Sagaut. Shallow water sound source localization using the iterative beamforming method in an image framework. Journal of Sound and Vibration, Elsevier, 2017, 395, pp.354 - 370. ⟨10.1016/j.jsv.2017.02.032⟩. ⟨hal-01527615⟩ Plus de détails...
Shallow water is a complicated sound propagation medium due to multiple reflections by water surface and bottom, imprecisely measured sound speed, noisy environment, etc. Therefore, in order to localize a shallow water sound source, classical signal processing techniques must be improved by taking these complexities into account. In this work, the multiple reflections and uncertain reflectivity of water bottom are explicitly modeled. In the proposed model, a measured signal is a mixture of the direct propagation from the source and the multiple reflections. Instead of solving the Helmholtz equation with boundary conditions of reflections, each signal is interpreted as a superposition of signals emitting from the physical source and its image sources in a free space, which results in a fast computation of sound propagation. Then, the source location, along with its amplitude, reflection paths and power loss of bottom reflection, is estimated via the iterative beamforming (IB) method, which alternatively estimates the source contributions and performs beamforming on these estimates until convergence. This approach does not need to compute the sound propagation for all the possible source locations in a large space, which thus leads to a low computational cost. Finally, numerical simulations are introduced to illustrate the advantage of the proposed model and the source estimation method. The sensitivity of the proposed method with respect to model parameter uncertainties is also investigated via a full uncertainty quantification analysis. The localization error of IB is proved to be acceptable in the given error range of sound speed and water depth. Besides, the IB source estimate is more sensitive to the sound speed while the matched-field processing methods have a stronger sensitivity to the water depth: this result can guide the choice of source localization method in different cases of model parameter uncertainties.
Xun Wang, Shahram Khazaie, Luca Margheri, Pierre Sagaut. Shallow water sound source localization using the iterative beamforming method in an image framework. Journal of Sound and Vibration, Elsevier, 2017, 395, pp.354 - 370. ⟨10.1016/j.jsv.2017.02.032⟩. ⟨hal-01527615⟩
Stationarity is a key tool in classical time series. In order to analyze the set-valued time series, it must be extended to the set-valued case. In this paper, stationary set-valued time series is defined via DpDp metric of set-valued random variables. Then, estimation methods of expectation and auto-covariance function of stationary set-valued time series are proposed. Unbiasedness and consistency of the expectation estimator and asymptotic unbiasedness of the auto-covariance function estimator are justified. After that, a special case of the set-valued time series, known as interval-valued time series, is considered. Two forecast methods of the stationary interval-valued time series are explicitly presented. Furthermore, the interval-valued time series is contextualized in the Box–Jenkins framework: an interval-valued autoregression model, along with its parameter estimation method, is introduced. Finally, experiments on both simulated and real data are presented to justify the efficiency of the parameters estimation method and the availability of the proposed model.
Xun Wang, Zhongzhan Zhang, Shoumei Li. Set-valued and interval-valued stationary time series. Journal of Multivariate Analysis, Elsevier, 2016, 145, pp.208 - 223. ⟨10.1016/j.jmva.2015.12.010⟩. ⟨hal-01450819⟩
Shahram Khazaie, Xun Wang, Pierre Sagaut. Localization of random acoustic sources in an inhomogeneous medium. Journal of Sound and Vibration, Elsevier, 2016, 384, pp.75 - 93. ⟨10.1016/j.jsv.2016.08.004⟩. ⟨hal-01375680⟩ Plus de détails...
In this paper, the localization of a random sound source via different source localization methods is considered, the emphasis being put on the robustness and the accuracy of classical methods in the presence of uncertainties. The sound source position is described by a random variable and the sound propagation medium is assumed to have spatially varying parameters with known values. Two approaches are used for the source identification: time reversal and beamforming. The probability density functions of the random source position are estimated using both methods. The focal spot resolutions of the time reversal estimates are also evaluated. In the numerical simulations, two media with different correlation lengths are investigated to account for two different scattering regimes: one has a correlation length relatively larger than the wavelength and the other has a correlation length comparable to the wavelength. The results show that the required sound propagation time and source estimation robustness highly depend on the ratio between the correlation length and the wavelength. It is observed that source identification methods have different robustness in the presence of uncertainties. Advantages and weaknesses of each method are discussed.
Shahram Khazaie, Xun Wang, Pierre Sagaut. Localization of random acoustic sources in an inhomogeneous medium. Journal of Sound and Vibration, Elsevier, 2016, 384, pp.75 - 93. ⟨10.1016/j.jsv.2016.08.004⟩. ⟨hal-01375680⟩
Xun Wang, Shahram Khazaie, Pierre Sagaut. Sound source localization in a randomly inhomogeneous medium using matched statistical moment method. Journal of the Acoustical Society of America, Acoustical Society of America, 2015, 138 (6), pp.3896. ⟨10.1121/1.4938238⟩. ⟨hal-01276517⟩ Plus de détails...
This paper investigates the problem of sound source localization from acoustical measurements obtained by an array of microphones. The sound propagation medium is assumed to be randomly inhomogeneous, being modelled by a random function of space. In this case, classical source localization methods (e.g., beamforming, near-field acoustical holography, and time reversal) cannot be used anymore. Therefore, an approach based on the statistical moments of acoustical measurement is proposed to solve the aforementioned problem. In this work, a Karhunen–Loève expansion is used so that the random medium can be represented by a small number of uncorrelated and identically distributed random variables. The statistical characteristics of the measurements in terms of probability density function and statistical moments are also studied. Then, the sound source is localized by minimizing the error of statistical moments between the real measurements obtained from the microphone array and the measurements simulated from an assumed source. Finally, a numerical example is introduced to justify the proposed method. This experiment shows that the random field can be replicated by a very small number of random variables, the statistical moments of measurements guarantee the convergence, and the source location can be accurately estimated using the proposed source localization method.
Xun Wang, Shahram Khazaie, Pierre Sagaut. Sound source localization in a randomly inhomogeneous medium using matched statistical moment method. Journal of the Acoustical Society of America, Acoustical Society of America, 2015, 138 (6), pp.3896. ⟨10.1121/1.4938238⟩. ⟨hal-01276517⟩
Journal: Journal of the Acoustical Society of America