Multi-Modal Signal Analysis for Underwater Acoustic Sound Processing

Talebpour, Faraz; Mozaffari, Saeed; Saif, Mehrdad; Alirezaee, Shahpour

doi:10.1109/ccece49351.2022.9918271

Cited by 2 publications

(5 citation statements)

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“…This paper extends our previous work presented in CCECE 2022 [1] by introducing a selective-modal algorithm architecture for localizing impulsive sound sources in shallow waters. Our proposed algorithm improves performance in lower signal-to-noise ratio (SNR) scenarios by selecting the best modal pairs.…”

Section: Introductionsupporting

confidence: 56%

“…In this paper, we provide a more detailed explanation of the localization formulas, propose a 2D unsynchronized localization scheme, analyze the performance of our algorithms using real recorded signals, and compare them with existing works. This paper extends our previous work presented in CCECE 2022 [1] by introducing a selective-modal algorithm architecture for localizing impulsive sound sources in shallow waters. Our proposed algorithm improves performance in lower signal-tonoise ratio (SNR) scenarios by selecting the best modal pairs.…”

Section: Introductionsupporting

confidence: 56%

“…The signal's energy radiates outwards, and therefore the solution will be H (1) 0 . Considering the radiation conditions, after substituting (11) in (7), we can derive the pressure equation based on the modal function as…”

Section: Underwater Acoustic Propagationmentioning

confidence: 99%

“…In this paper, we build upon our previous work published in [1] and introduce novel advancements to the field of underwater localization. Specifically, we extend our research by incorporating the utilization of multiple hydrophones for two-dimensional localization.…”

Section: Introductionmentioning

confidence: 99%

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Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Talebpour¹,

Mozaffari²,

Saif³

et al. 2023

Adv. sci. technol. eng. syst. j.

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Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a localization method for a noisy, shallow water environment. Our proposed algorithm employs modal selection to process the most noiseresistive dispersion curves, improving the accuracy and noise-resistivity of the existing methods. Moreover, we proposed a 2D localization method with multiple unsynchronized hydrophones and minimal hardware requirements and limitations. Furthermore, we analyzed the effects of underwater ambient noise on the accuracy of the proposed method, using simulated and real recorded explosion and whale sounds, and compared our algorithm's localization performance with others. Simulation results show increased localization accuracy of 30m for the recorded explosion sound and 360m for the Whale sound.

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Section: Introductionsupporting

confidence: 56%

Section: Introductionsupporting

confidence: 56%

Section: Underwater Acoustic Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Talebpour¹,

Mozaffari²,

Saif³

et al. 2023

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The solution to the SLAM problem is considered to be one of the fundamental requirements to achieve true robot autonomy. The SLAM could be extremely useful during scenarios when the global positioning system (GPS) is inaccessible, e.g., in environments such as indoor [ 2 ], drone [ 3 ], underwater [ 4 ], forest canopy [ 5 ], medical [ 6 ], etc. However, the interdependence nature of localization and mapping makes SLAM a complex and challenging problem in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Occupancy Map-Merging for Collaborative SLAM

Sunil

Mozaffari

Singh

et al. 2023

Sensors

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One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

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Multi-Modal Signal Analysis for Underwater Acoustic Sound Processing

Cited by 2 publications

References 0 publications

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Feature-Based Occupancy Map-Merging for Collaborative SLAM

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