Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode Extraction

Liang, Guolong; Zhang, Yifeng; Zhang, Guangpu; Jia, Feng; Zheng, Ce

doi:10.3390/s18113692

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…where k is the slope of the line, t is the y-intercept, and k and t are undetermined parameters. The least-squares (LS) [18], total-least-squares (TLS) [19], and robust-least-squares (RLS) [20,21] linear fitting methods are three well-developed fitting methods. the LS method is the most commonly used.…”

Section: Linear Fitting Methods For Registration Errormentioning

confidence: 99%

Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Cao

Yuan

et al. 2020

Sensors

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Temporally-spatially modulated Fourier transform imaging spectrometers (TSMFTISs) provide high-throughout-type push-broom spectrometry with both temporal and spatial modulation features. The system requires strict registration between the detector and the interferogram. However, registration errors are unavoidable and directly change the corresponding optical path difference values of the interferogram. As a result, the interferogram should be corrected before restoring the spectrum. In order to obtain the correct optical path difference (OPD) values, an online registration error correction method based on robust least-square linear fitting is presented. The model of the registration error was constructed to analyze its effect on the reconstructed spectra. Fitting methods were used to obtain correct optical path difference information. Simulations based on the proposed method were performed to determine the influence of the registration error on the restored spectra and the effectiveness of the proposed correction method. The simulation results prove that the accuracy of the recovered spectrum can be improved after correcting the interferogram deviation caused by the registration error. The experimental data were also corrected using the proposed methods.

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Section: Linear Fitting Methods For Registration Errormentioning

confidence: 99%

Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Cao

Yuan

et al. 2020

Sensors

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“…This is because the surface vessel and submarine can be distinguished according to the depth. This is another example where a simulated sound field based on an acoustic propagation model is mainly used (Conan et al, 2016;Conan et al, 2017;Liang et al, 2018). In particular, Conan et al (Conan et al, 2017) conducted a study to distinguish the depth of a sound source by using the propagation characteristics of sound waves in a specified environment.…”

Section: Passive Target Localizationmentioning

confidence: 99%

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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Underwater acoustics is a scientific domain that involves the study of the phenomena of sound waves in water, including their generation, propagation, and reception. Specifically, the sound navigation and ranging (SONAR) system is utilized to investigate underwater communication and target detection and to study marine resources and the environment; further, it is utilized to measure and analyze sound sources in water. The main objective of underwater acoustics-based remote sensing is the indirect acquisition of information on underwater targets of interest using acoustic data. At present, highly advanced data-driven machine-learning techniques are being applied in various ways for extracting information from acoustic data. The techniques closely related to these applications are introduced in the first part of this paper (Yang et al., 2020). This paper presents a detailed review of the applications of machine learning in underwater acoustics and passive SONAR signal processing. 2. Passive SONAR Signal Processing 2.1 Passive Target Detection and Identification Signals measured by a passive SONAR system exhibit fluctuations owing to irregular noises in the ocean. This hinders target signal detection. The conventional signal processing method for detecting target signals is based on the Neyman-Pearson criterion (Nielsen, 1991). As the probability distribution of the received signals, including the target signals, differs from that of the noise signals, the probability ratio that is set according to the presence of the target signal at the time of observation is compared with a preset value. This helps determine whether the target signal is included in the observed time period. This technique can be expanded to detect the target signal by comprehensively analyzing all the signals measured in the time domain of interest as well as signals observed at a specific time. In general, techniques for detecting a target signal through comparison with a threshold value have a disadvantage: false alarms can occur frequently, particularly in the scenario of a low signal

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“…Asam borat (206)(207) ikut serta dalam penyerapan pada bunyi frekuensi (113)(114)(115)(116)(117) rendah pada air laut.…”

Section: Analisis Gapunclassified

Boric Acid (H3(BO3): Recognize The Molecular Interactions in Solutions

Dwynda¹,

Zainul²

2018

Preprint

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Asam borat biasa dikenal dengan asam boraks merupakan suatu monobasa dari asam Lewis boron lemah yang dapat digunakan sebagai antiseptik, insektisida, penyangga pH, atau penyerap neutron. Asam borat larut dalam air mendidih. Ketika senyawa ini dipanaskan pada suhu melebihi 170OC senyawa ini akan terdehidrasi kemudian membentuk HBO2 atau asam metaborat . Senyawa dengan rumus kimia H3BO3 ini terdapat dalam bentuk serbuk putih yang larut dalam air dan kristal yang tidak berwarna. Penilitian ini bertujuan untuk mengetahui struktur, karakteristik, bentuk molekul dan jenis ikatan dari asam borat. Untuk metode literasi jurnal digunakan aplikasi EndNote yang dijelaskan melalui fishbone state of art. Sedangkan metode yang digunakan untuk melihat hasil optimasi, energi serta gambar dari molekul digunakan metode komputasi dengan software ChemOffice 15.0. Berdasarkan metode ini diperoleh energi kinetik rotasi senyawa 0.3165, dipole/dipole 1.2329 dan energi total 5.1769 kcal/mol. Untuk konduktivitas, kecepatan hanyut, gerakan ion, dilakukan dengan menggunakan metode perhitungan dengan rumusan hasilnya dan beberapa soal yang dibuatkan grafiknya. Secara termodinamika, asam borat pada keadaan standar dan tekanan 1 atm memiliki ∆HoR -5351 kJ / kmol, ∆G°f -122.462 kJ / kmol dengan konstanta kecepatan reaksi k= 8,8727 L/kmol.s. dan Cp pada suhu 100oC sebesar 35,93 J/kgK serta Ho -16.636 J/mol. Proses pembuatan asam borat berlangsung secara spontan dapat dilihat melalui ∆G°f yang bernilai negatif dan terjadi reaksi eksoterm dapat dilihat berdasarkan perolehan ∆HoR yang negatif.

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Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode Extraction

Cited by 7 publications

References 20 publications

Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

Boric Acid (H3(BO3): Recognize The Molecular Interactions in Solutions

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