Deconvolution by Homomorphic and Wiener Filtering
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Abstract : This study is concerned with deconvolution methods applied to underwater propagation in shallow water, whereby the received signal is modelled as the convolution between the transmitted pulse and the medium impulse response. The aim of the method is to extract information on backscattering, travel time delays, boundary reflection and refraction from the received signal on a point receiver or an array for both seismic and active sonar data. Since experimental data are generally mixed phase, due in part to the multiple reflections (bottom and surface), the conventional linear filtering which assumes the minimum phase property, loses in efficacy. In order to handle this mixed phase characteristic of the data, we proceed in two steps. We first apply a homomorphic filter (complex cepstrum) to deconvolve the wavelet. Then we deconvolve the medium impulse response by means of Wiener filter. The efficacy of the method is shown on both simulated and real data for explosive and active sonar data. Keywords: Acoustic sonar signals; Scattering; Seismic waves; Cepstrum technique; Bottom reflection; Low frequency; Wave propagation; Seismic data; Towed array.Keywords:
Impulse response
Wiener deconvolution
Cepstrum
Wiener filter
Finite impulse response
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The ocean waveguide is temporally and spatially varying. An acoustic signal transmitted through the ocean or scattered from objects will get randomized. Estimating parameters of sonar, the environment, and scatterers requires a statistical approach that incorporates medium uncertainties into the signal analysis. Here we discuss source level, transmission loss (TL), and reverberation model calibration using data acquired with a long-range sonar on the New Jersey STRATAFORM. Broadband acoustic data measured by a desensitized hydrophone in a towed horizontal receiving array at numerous locations from a vertical source array are analyzed. The match-filtered data are compared to the expected TL output from a parabolic equation model that accounts for bathymetric variations. A maximum likelihood estimator is implemented to provide a global inversion of the data for source level, attenuation due to scattering, and match-filter degradation in the multi-modal ocean waveguide. An estimate is also provided of the coherence bandwidth for broadband acoustic signal transmission in this environment. A challenge in calibrating bottom reverberation models with sonar data lies in separating the scattered intensity from moving objects, such as fish groups, and distinguishing them from the statistically stationary background reverberation. An approach is presented for this purpose.
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Underwater Acoustics
Sea trial
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Describes a real-time passive-sonar simulator. Most of the processing involved in the simulator is carried out in the frequency domain; the arguments for doing this are presented. The spectrum of noise radiated by a target vessel can be modelled as a frequency spectrum in which the spectral components are generated stochastically. Propagation effects are also modelled in the frequency domain. While it is possible to produce a model containing multiple paths where differential time delays and transmission losses are incorporated, it is more efficient to generate the propagation model stochastically. Similarly, the spectrum of the ambient noise is also generated stochastically. This noise spectrum is added in the frequency domain to the received signal spectrum and then an inverse Fourier transformation is made to generate a time waveform which models the output from a single hydrophone. >
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Sound that travels through the ocean from a source to a receiver is commonly distorted by multipath acoustic propagation. The severity and details of this distortion are determined by the sound channel’s characteristics, which may not be known. However, recovery or reconstruction of undistorted signals from recordings made in unknown complex multipath environments—a process commonly referred to as blind deconvolution—is advantageous in many applications of underwater acoustics. This paper presents a simple and robust means to achieve blind deconvolution in unknown sound channels with an array of receiving transducers. The technique, artificial time reversal (ATR), can be effective when there is a linear relationship between frequency and the phase of the low-order propagating modes of the sound channel. Broadband simulations of ATR in a generic shallow ocean sound channel show that the maximum correlation between the original signal and the reconstructed signal may approach 100% for signal pulses having a 258 Hz bandwidth and a 500 Hz center frequency. Application of ATR to ocean sound-propagation measurements produces original-to-reconstructed signal correlations from 80% to 95%. Possible extensions and improvements of ATR are discussed.
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Distortion (music)
Underwater Acoustics
Underwater acoustic communication
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This paper presents an approach to array element localization (AEL) for towed marine seismic arrays based on regularized inversion of direct and bottom-reflected acoustic ray travel times picked from recorded seismic sections. Depth-sensor measurements at a number of points along the array are included as a priori estimates (with uncertainties) in the inversion. The smoothest array shape consistent with the acoustic data and prior estimates is determined by minimizing the array curvature or roughness. A smooth array shape is physically reasonable; in addition, minimizing curvature provides a priori information about the correlation between hydrophone positions that allows the estimation of both the offset and depth of hydrophones that record only one (or even no) acoustic arrival due to the shadowing effects of water-column refraction or reflection from arbitrary bathymetry. The AEL inversion is applied to a 102-sensor, 1.2-km towed array to correct receiver positions in the seismic velocity analysis of a seabed gas hydrate survey.
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Seabed
Vertical seismic profile
Seismic survey
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In oil and gas exploration, seismic arrays are deployed by geophycisists to image the subsurface. For passive seismic applications, the data recorded by the sensor array may contain velocity and angle information of the propagating seismic wave. This information can be used to infer the properties of material in different earth layers. In order to find the velocity and arrival angle, beamforming algorithms are applied to estimate the wavenumber-frequency spectrum for the seismic signals. The propagating seismic wave field consists of body waves and surface waves. In some applications, surface waves are interpreted as noise, thus filtering is required to remove the surface waves before or during the implemention of beamforming algorithms. In this thesis, we first introduce a data model. Then several beamforming algorithms based on the data model are discussed, and the performance of the different algorithms is evaluated. Capon beamforming as adopted in seismics has limitations. Robust Capon beamforming which can overcome these limitations is explained in the thesis. For filtering of the surface waves, we propose to first reconstruct the irregularly sampled spatial signal into a uniform array, then design a velocity filter to remove the unwanted low-speed noise (surface waves).
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Wavenumber
Seismic Noise
Passive seismic
Rayleigh Wave
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Echoes are generated, and compared, for extended targets in a shallow water environment using the total target–medium broadband frequency response function and the frequency representation of the transmitted signal. The two-way medium response is calculated using the generic sonar model (GSM) environmental model. The target frequency response is found using a model of distributed targets and elastic spheres. The combination of the target and medium frequency responses gives the full frequency response, or form function, for the target–medium system. The product of the total form function with the frequency spectrum of the incident signal is computed and an inverse FFT is performed to predict the received signal, or echo, from the object. [This work was sponsored by the Office of Naval Research—Technology Directorate.]
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Echo (communications protocol)
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Ambiguity function
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In this paper we present a time-frequency approach for acoustic seabed classification. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), one of the five sonar systems in the AN/AQS-20. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm in order to determine the impulse response of the sediment. The Fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Singular Value Decomposition and statistical properties of the Wigner and Choi-Williams distributions of the bottom impulse response are employed as features which are, in turn, used for classification. The Wigner distribution can be thought of as a signal energy distribution in joint time-frequency domain. Results of our study show that the proposed technique allows for accurate sediment classification of seafloor bottom data. Experimental results are shown and suggestions for future work are provided.
Impulse response
Seabed
Sonar signal processing
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Underwater Acoustics
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An important problem in active sonar is the classification of objects from their returned scattered acoustic pressure field. Since in active sonar the insonifying waveform is usually broadband the received pressure is processed by matched filtering. After matched filtering an object's spectrum often contains sufficient information for classification. However, when objects are similar motion modifies their spectra enough so that the probability of false classification increases. In this paper the object's motion is first extracted and then sequential classification proceeds based on its measured fourth-order cumulant spectrum for each active sonar return and known fourth-order cumulant spectra of objects with similar velocity. The fourth-order cumulant spectrum is used because of its unique properties of noise suppression and feature extraction.< >
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