Contributions to passive acoustic oceanic tomography. II. Extraction of propagation features from single hydrophone measurement
20 Jun 2005-Vol. 2, pp 914-918
TL;DR: In this paper, the authors used matched delay, matched field and matched impulse response inversion processing to estimate acoustic properties of the water column from the measurement of a propagated known acoustic waveform between fixed sources and receivers.
Abstract: Acoustic tomography is a way to produce a fast, accurate and cheap monitoring of water mass. This monitoring requires an inversion procedure made of two steps. The first one is to estimate acoustic properties (such as the sound speed profile of the water column) from the measurement of a propagated known acoustic waveform between fixed sources and receivers. Then a second step consists in inferring some physical ocean parameters (temperature, bottom nature) from these previous estimated acoustic characteristics. Large scales deep water and small scales shallow water configurations have been successfully studied and associated to matched delay, matched field and matched impulse response inversion processing.
References
More filters
TL;DR: A signal-dependent kernel that changes shape for each signal to offer improved time-frequency representation for a large class of signals is proposed and an efficient scheme based on Newton's algorithm for finding the optimal kernel is developed.
170 citations
TL;DR: A time-frequency formulation is proposed for the optimum detection of Gaussian signals in white Gaussian noise and it is shown that the corresponding receivers generally take the form of a correlation between time- Frequency structures, matching mathematical optimality with a physically meaningful interpretation.
Abstract: A time-frequency formulation is proposed for the optimum detection of Gaussian signals in white Gaussian noise. By choosing the Wigner-Ville distribution as the basic time-frequency tool, it is shown that the corresponding receivers generally take the form of a correlation between time-frequency structures, matching mathematical optimality with a physically meaningful interpretation. The case of low SNR is examined and various examples are considered: deterministic signal, Rayleigh fading signal, random jitter, and random time-varying channel. A general class of time-frequency receivers is proposed which admits as limiting cases different known structures, and its suboptimum performance is evaluated. Possible extensions to more elaborate situations (including parameter estimation) are mentioned. >
161 citations
TL;DR: A new algorithm for blindly estimating the impulse response of a channel offers a new way for locating sounds and making tomographic maps of the environment without any requirement for a model for the propagation of sound such as is needed for focalization and matched field processing.
Abstract: Calling animals are located using widely distributed receivers, and the sounds from the animals are used to map the sound speed and wind fields by means of tomography. In particular, two Red-Winged Blackbirds Agelaius phoeniceus are correctly located within a meter using recordings from five receivers spread over a 20 by 30 m region. The demonstration hinges on two new developments. First, a new algorithm for blindly estimating the impulse response of the channel is shown capable of estimating the differences in the time of first arrivals at two receivers. Since it is known that the first arrivals travel along nearly straight paths, the difference in time constrains the animal’s location to a hyperboloid, and the animal is located by intersecting hyperboloids from many pairs of receivers. Second, in order to accurately find the intersection point and map the sound speed and wind fields using tomography, a nonlinear equation is solved. The new algorithm for blindly estimating the impulse response of a channel offers a new way for locating sounds and making tomographic maps of the environment without any requirement for a model for the propagation of sound such as is needed for focalization and matched field processing.
56 citations
"Contributions to passive acoustic o..." refers background or methods in this paper
...For this, location is estimated by blind triangulation with relative time of arrival between all the hydrophones of the coastal array thanks to hyperboling fixing method described in [5] ....
[...]
...More details on the data can be found in [5] ....
[...]
Journal Article•
TL;DR: In this paper, the detection and localization of marine mammals using passive acoustics is explored for two critical habitats in Eastern Canada, and two-dimensional hyperbolic localization is performed on time differences of arrivals of specific calls on grids of coarsely spaced autonomous recorders and on a shore-linked coastal array of closely spaced hydrophones.
Abstract: The detection and localization of marine mammals using passive acoustics is explored for two critical habitats in Eastern Canada. Two-dimensional hyperbolic localization is performed on time differences of arrivals of specific calls on grids of coarsely spaced autonomous recorders and on a shore-linked coastal array of closely spaced hydrophones. Delays are computed from cross-correlation and spectrogram cross-coincidence on signals enhanced with high-frequency emphasis and noise spectral suppression techniques. The outcomes and relative performance of the two delay estimation methods are compared. The difficulties encountered under the particular conditions of these two environments are discussed for the point of view of automated localisation for monitoring whales.
20 citations
13 May 2002
TL;DR: Blind deconvolution is presented in the underwater acoustic channel context, by time-frequency processing, which has proved to overcome the typical ill-conditioning of single sensor deterministic deconVolution techniques.
Abstract: Blind deconvolution is presented in the underwater acoustic channel context, by time-frequency processing. The acoustic propagation environment was modelled as a multipath propagation channel. For noiseless simulated data, source signature estimation was performed by a model-based method. The channel estimate was obtained via a time-frequency formulation of the conventional matched-filter. Simulations used a ray-tracing physical model, initiated with at-sea recorded environmental data, in order to produce realistic underwater channel conditions. The quality of the estimates was 0.793 for the source signal, and close to 1 for the resolved amplitudes and time-delays of the impulse response. Time-frequency processing has proved to overcome the typical ill-conditioning of single sensor deterministic deconvolution techniques.
17 citations