The least squares concept in reducing noisy signal of single-beam acoustic systems: Ocean depth measurement to support maritime defense systems

Authors

  • Annisa Risda Alivia Komando Pembinaan Doktrin, Pendidikan dan Latihan TNI Angkatan Laut, The Indonesian Navy
  • Syasya Qonita Azizah Indonesia Defense University
  • Alok Shukla Indian Institute of Technology Bombay

DOI:

https://doi.org/10.58524/app.sci.def.v3i3.643

Keywords:

Least Mean Square, Signal, Noise Reduction, Single Beam

Abstract

Indonesia's vast ocean territory presents both opportunities and security challenges, requiring robust maritime defense. Effective sea defense includes surface patrols with naval vessels and aircraft, alongside underwater surveillance using submarines and detection systems. Advanced acoustic technology, such as Single Beam Echo Sounder (SBES) sonar, is essential for underwater depth measurement. However, environmental noise often disrupts sonar recordings, necessitating noise reduction techniques. This study applies the Least Mean Square (LMS) filter, an adaptive algorithm that adjusts filter coefficients based on error minimization. Its real-time adaptability enhances noise suppression, improving sonar signal quality. The results indicate that the LMS filter achieves an optimal Signal-to-Noise Ratio (SNR) of 6.7248 dB, surpassing other methods. Furthermore, it accurately identifies signal delays, crucial for precise depth measurement. Enhancing underwater acoustic technology through LMS filtering supports improved hydrographic surveys, benefiting scientific research, commercial navigation, and military operations in securing Indonesia’s maritime domain.

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Published

2025-12-30

How to Cite

Alivia, A. R., Azizah, S. Q., & Alok Shukla. (2025). The least squares concept in reducing noisy signal of single-beam acoustic systems: Ocean depth measurement to support maritime defense systems. International Journal of Applied Mathematics, Sciences, and Technology for National Defense, 3(3), 161-170. https://doi.org/10.58524/app.sci.def.v3i3.643