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  • 1. Karasalo, I.
    et al.
    Skogqvist, Patrik
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Acoustic scattering from submerged and buried objects2006In: Acoustic Sensing Techniques for the Shallow Water Environment: Inversion Methods and Experiments, Springer Netherlands, 2006, p. 137-153Chapter in book (Other academic)
    Abstract [en]

    Two techniques are described for numerical prediction of transient scattering by 3D objects in the water column or buried in the bottom sediment. In the first method,b the scattering problem is formulated as a boundary integral equation (BIE) for a three-dimensional body inside a range-independent layered fluid-solid medium. The Green's function of the layered medium is computed by an accurate transform integral method employing exact finite elements and adaptive high-order numerical integration. A Burton-Miller formulation of the BIE is used, leading to a linear combination of weakly singular and 'hypersingular' BIE free from artificial singularities. The BIE is discretized using B-spline basis functions, global high-order integration and point collocation, and then solved iteratively with a preconditioned generalized minimum residual (GMRES) method. The second method is a fast approximative scattering model based on a combination of acoustic ray tracing and Kirchhoff's approximation of the scattered field. Both methods are formulated in the frequency domain, and Fourier synthesis is used for computing transient fields. Examples of scattered pulses predicted by the two techniques are presented. The predicted fields are compared with data from experiments in which a semi-buried object was probed by a ROV-mounted parametric sonar and the scattered field was registered by bistatically located receivers. A method for identification of parameters of the scattering objects, based on the fast scattering model combined with an algorithm for global nonlinear optimization is described. Both the optimization method and the fast scattering model are highly parallelizable, and are implemented on workstation clusters under MPICH, allowing for convenient handling of also computationally demanding broadband excitations. Some examples of parameter inversion results using experimental data from the EU project SITAR are presented. ©

  • 2. Karasalo, I.
    et al.
    Skogqvist, Patrik
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Object identification by bistatic acoustic scattering2005Conference paper (Refereed)
    Abstract [en]

    An inversion technique is described for remote estimation of parameters of submerged or buried objects using a ROV-mounted directive acoustic source and a separately located vertical receiver array. The transmitter emits a train of pulses towards the object, and the scattered echoes are recorded at the receivers for subsequent parameter estimation. Parameters of the object are estimated by nonlinear global minimization of the misfit between the experimentally observed and model-predicted time-series. Two global minimization methods, a genetic algorithm (GA) and a differential evolution algorithm (DE), are considered. A fast approximative technique for computing the scattered field, the RK (Ray-Kirchoff) method, is described and used as forward model at the parameter search. The accuracy of the transient scattered field predicted by the RK method is assessed in a model case, using an accurate full-field boundary integral equation (BIE) method as reference. Experimental data are presented from a sea trial within the EU SITAR project in the Stockholm archipelago in Sept 2003. In the trial, a semi-buried box-shaped object was investigated using a ROV-mounted TOPAS 120 parametric sonar as source. A fitness function suitable for the characteristics of the experimental data and the accuracy properties of the RK method is formulated, and the inversion methods are applied on the data to estimate, in a step-wise manner, seven physical parameters of the object; range, depth, roll, yaw, pitch, density and soundspeed. The estimated parameter values are shown to reduce the model-data misfit significantly compared with those based on prior information only.

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