Direct regressions for underwater acoustic source localization in fluctuating oceans

Lefort, Riwal; Real, Gaultier; Drémeau, Angélique

doi:10.1016/j.apacoust.2016.10.005

Cited by 58 publications

(23 citation statements)

References 21 publications

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“…Denoting the number of training samples as M , the dimension of the training set is M ×F . n f is selected empirically as 20 (based on numerical experiments on the validation set, choice of n f from [10,25] gave similar performance). Note that the magnitude in Eq.…”

Section: A Input Data Preprocessingmentioning

confidence: 99%

“…In the first step, the range is discretized into coarse grids with low range resolution. The range interval 1-20 km is discretized every 5 km to 4 classes: [1,5), [5,10), [10,15), [15,20]. Every input signal is clas-…”

Section: Localization Algorithmmentioning

confidence: 99%

“…Raw signa ls [10,15) [ 15,20] [5,10) For the second step, the mean absolute error (MAE) is used to evaluate the prediction performance…”

Section: Pre Processed Signalsmentioning

confidence: 99%

See 2 more Smart Citations

Deep-learning source localization using multi-frequency magnitude-only data

Niu

Gong

Ozanich

et al. 2019

The Journal of the Acoustical Society of America

114

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A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach.

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Section: A Input Data Preprocessingmentioning

confidence: 99%

Section: Localization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Deep-learning source localization using multi-frequency magnitude-only data

Niu

Gong

Ozanich

et al. 2019

The Journal of the Acoustical Society of America

114

View full text Add to dashboard Cite

show abstract

“…The location estimator for y S 1 can be obtained in a similar way. The location estimator (7) exploits the fact that asû 1 is symmetric, the reflected correlation (6), which is the correlation betweenû 1 and a reflected and shifted version ofû 1 , is maximized at the source location. Therefore, the estimator x S 1 (û 1 ) tries to the suppress the perturbation from the noise by correlating over all the entries ofû 1 .…”

Section: B Location Estimator Exploiting Property Of Symmetrymentioning

confidence: 99%

Rotated eigenstructure analysis for source localization without energy-decay models

Chen

Mitra

2017

2017 22nd International Conference on Digital Signal Processing (DSP)

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Abstract-Herein, the problem of simultaneous localization of two sources given a modest number of samples is examined. In particular, the strategy does not require knowledge of the target signatures of the sources a priori, nor does it exploit classical methods based on a particular decay rate of the energy emitted from the sources as a function of range. General structural properties of the signatures such as unimodality are exploited. The algorithm localizes targets based on the rotated eigenstructure of a reconstructed observation matrix. In particular, the optimal rotation can be found by maximizing the ratio of the dominant singular value of the observation matrix over the nuclear norm of the optimally rotated observation matrix. It is shown that this ratio has a unique local maximum leading to computationally efficient search algorithms. Moreover, analytical results are developed to show that the squared localization error decreases at a rate n −3 for a Gaussian field with a single source, where n(log n)2 scales proportionally to the number of samples M .

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“…In the last decades, the acoustic based locating method has played a main role in underwater localization [1,2]. However, the complexity and uncertain characteristics of underwater environments, such as the varying temperature and density, multi-path propagation, Doppler effect, and propagation delay, seriously influence the acoustic propagation and underwater channel [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Multiple Signal Classification Algorithm Based Electric Dipole Source Localization Method in an Underwater Environment

Xue

et al. 2017

Symmetry

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A novel localization method based on multiple signal classification (MUSIC) algorithm is proposed for positioning an electric dipole source in a confined underwater environment by using electric dipole-receiving antenna array. In this method, the boundary element method (BEM) is introduced to analyze the boundary of the confined region by use of a matrix equation. The voltage of each dipole pair is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields based localization method, which can be easily implemented in practical engineering applications. Then, a global-multiple region-conjugate gradient (CG) hybrid search method is used to reduce the computation burden and to improve the operation speed. Two localization simulation models and a physical experiment are conducted. Both the simulation results and physical experiment result provide accurate positioning performance, with the help to verify the effectiveness of the proposed localization method in underwater environments.

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Direct regressions for underwater acoustic source localization in fluctuating oceans

Cited by 58 publications

References 21 publications

Deep-learning source localization using multi-frequency magnitude-only data

Deep-learning source localization using multi-frequency magnitude-only data

Rotated eigenstructure analysis for source localization without energy-decay models

Multiple Signal Classification Algorithm Based Electric Dipole Source Localization Method in an Underwater Environment

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