Ship localization in Santa Barbara Channel using machine learning classifiers

Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

doi:10.1121/1.5010064

Cited by 128 publications

(28 citation statements)

References 13 publications

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“…Recent interest in ML first appeared for target classification. 42,172 Studies of ocean source localization using ML appeared soon thereafter, 4,152 including applications to experimental data for broadband ship localization, 151 and target characterization. 173 Recently, studies have examined underwater source localization with CNNs 174 and DL, 175,176 taking advantage of 2D data structure, shared weighting, and huge modelgenerated datasets.…”

Section: Source Localization In Ocean Acousticsmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

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424

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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Section: Source Localization In Ocean Acousticsmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

Self Cite

424

126

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show abstract

“…They used the group method of data handling (GMDH) neural networks and an adaptive network-based fuzzy inference system (ANFIS) with good results. Niu et al [26] used the feed-forward neural network (FFNN) and support vector machine (SVM) classifiers to infer ship location from acoustical data in a deep waterway channel. Bannari et al [27] suggested an approach for bathymetric mapping of shallow water of Arabian Gulf near Bahrain using the kriging procedure.…”

Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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“…Most recent machine learning methods are data-hungry and the successful applications can be found especially in image processing, which is largely driven by the recently openly available huge volume of image datasets that could consist of millions of items [29]. In acoustic studies, most recent works have been primarily focused on acoustic signal processing [30][31][32][33] partly due to the presumed analogy with the imaging processing applications. To study new problems, the corresponding datasets should be prepared in advance to enable machine learning.…”

Section: Introductionmentioning

confidence: 99%

Deep neural networks for waves assisted by the Wiener–Hopf method

Huang

2020

Proc. R. Soc. A.

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In this work, the classical Wiener–Hopf method is incorporated into the emerging deep neural networks for the study of certain wave problems. The essential idea is to use the first-principle-based analytical method to efficiently produce a large volume of datasets that would supervise the learning of data-hungry deep neural networks, and to further explain the working mechanisms on underneath. To demonstrate such a combinational research strategy, a deep feed-forward network is first used to approximate the forward propagation model of a duct acoustic problem, which can find important aerospace applications in aeroengine noise tests. Next, a convolutional type U-net is developed to learn spatial derivatives in wave equations, which could help to promote computational paradigm in mathematical physics and engineering applications. A couple of extensions of the U-net architecture are proposed to further impose possible physical constraints. Finally, after giving the implementation details, the performance of the neural networks are studied by comparing with analytical solutions from the Wiener–Hopf method. Overall, the Wiener–Hopf method is used here from a totally new perspective and such a combinational research strategy shall represent the key achievement of this work.

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Ship localization in Santa Barbara Channel using machine learning classifiers

Cited by 128 publications

References 13 publications

Machine learning in acoustics: Theory and applications

Machine learning in acoustics: Theory and applications

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

Deep neural networks for waves assisted by the Wiener–Hopf method

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