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Hybrid Acoustic System for Underwater Target Detection and Tracking

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Abstract

The detection and localization of underwater targets, including submarines and Unmanned Underwater Vehicles (UUVs), play a crucial role in various maritime applications such as defence, surveillance, and resource exploration. This paper presents a comprehensive approach for the detection, localization, and tracking of underwater targets utilizing a combined passive and active SONAR system. The passive SONAR system offers the advantage of stealthy detection without emitting active acoustic signals. Once a target is detected, an active SONAR tracking system is employed to precisely localize and track the target's movements. This system combines the results of the MUSIC algorithm, utilized for direction-of-arrival estimation, with tracking algorithms. The suggested method combines the advantages of passive and active SONAR systems to successfully identify, localise, and track targets in aquatic environments. The combination of these systems improves the capabilities of underwater surveillance and defence systems and offers thorough situational awareness. Tracking algorithms such as the Extended Kalman filter and Interactive multiple models are used to track the target and the performance of these tracking algorithms are compared.

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Data availability

All data generated or analysed during this study are included in this published article.

Code availability

For Simulation MATLAB R2021a has been used.

Abbreviations

x k | k − 1 :

Predicted state estimate at time k

P k − 1 | k − 1 :

Predicted covariance estimate

F k :

Jacobian matrix of F

P k − 1 | k − 1 :

Error covariance matrix at time k − 1

Q k :

Process noise covariance matrix

\({\widetilde{Y}}_{k}\) :

Innovation or measurement residual at time k

\({z}_{k}\) :

Measurement at time k

h:

Nonlinear measurement function.

\({S}_{k}\) :

Measurement residual covariance matrix at time k

\({H}_{k}\) :

Jacobian matrix of H

\({K}_{k}\) :

Kalman gain at time k

\({\widehat{x}}_{k|k}\) :

Updated state estimate at time k

\({R}_{12}\)(τ):

Cross correlation of signals \({h}_{1}\)(t) and \({h}_{2}\)(t)

\({R}_{k}\) :

Measurement noise covariance matrix

\({P}_{k|k}\) :

Updated error covariance matrix at time k

\({N}_{s}\) :

External Noise

\({R}_{x}\) :

Covariance matrix

\({R}_{s}\) :

Signal Correlation Matrix

\({P}_{mu}(\uptheta )\) :

Spatial spectrum

\({u}_{k}\) :

Control input at time k

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India

    G. Sumithra, N. Ajay, N. Neeraja & K. Adityaraj

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  1. G. Sumithra
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  2. N. Ajay
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  3. N. Neeraja
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  4. K. Adityaraj
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Contributions

All the authors contributed to performing the study of the paper and reviewed the manuscript. SG guided the entire project.AN performed the necessary simulation in Matlab and wrote the main manuscript. NN and AK helped with the manuscript.

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Correspondence to N. Ajay.

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Sumithra, G., Ajay, N., Neeraja, N. et al. Hybrid Acoustic System for Underwater Target Detection and Tracking. Int. J. Appl. Comput. Math 9, 149 (2023). https://doi.org/10.1007/s40819-023-01621-4

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