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A Machine Learning-Based Approach for Auto-Detection and Localization of Targets in Underwater Acoustic Array Networks | IEEE Journals & Magazine | IEEE Xplore

A Machine Learning-Based Approach for Auto-Detection and Localization of Targets in Underwater Acoustic Array Networks


Abstract:

The localization and tracking of underwater objects have many applications. In a proactive underwater sensor array, some nodes will periodically broadcast linear frequenc...Show More

Abstract:

The localization and tracking of underwater objects have many applications. In a proactive underwater sensor array, some nodes will periodically broadcast linear frequency modulated (LFM) signals, which will hit the targets, get reflected and received by the other nodes. Depending on the target's position and velocity, the received signals will also be LFM signals of different frequencies and frequency rates. We can use the Fractional Fourier Transform (FrFT) to analyze the received signal's spectrum and find the peak. Based on the location of the peak, the target's distance and radial velocity can be estimated. However, the accuracy is highly dependent on the sampling interval of the spectrum. Smaller sampling interval leads to higher accuracy but also induces considerable complexity. To overcome this issue, we propose a machine learning-based approach to automatically detect the existence of the target, and roughly estimate the peak's location if targets exist. Then over-sampling can be conducted for a small area around the peak, leading to improved accuracy and reduced complexity. The idea is based on the following observation: if a target exists, we will be able to observe an “X” pattern on the spectrum. Extensive simulations are conducted to verify the effectiveness of the proposed architecture.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 69, Issue: 12, December 2020)
Page(s): 15857 - 15866
Date of Publication: 06 November 2020

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I. Introduction

The detection, localization, and tracking of underwater objects have numerous civil and military applications, such as oceanic environmental monitoring, offshore oil leakage detection, underwater rescue, and many others. This is becoming not only important but also possible with the development of the future internet of underwater things (IoUT). The underwater nodes form arrays and can be used to keep tracking objects in large water areas.

References

References is not available for this document.