Robust Normalization Algorithms for Low-Frequency Active Sonar Signals

Abstract

Normalization is the process of forming a signal-to-noise ratio (SNR) from the raw signal power received by a sonar system. It is considered to be an essential part of sonar systems, especially under non-alerted conditions, and it may contribute significantly to the overall effectiveness of a surveillance system. However, the effectiveness of the normalization process is reliant upon an accurate estimate of the noise only reference; under real circumstances, the received data in general contains both noise and signal elements that can be difficult to separate. This report considers two algorithms for providing an adaptive estimate of the noise only reference for the normalization processors of active sonar signals. A 'soft' background noise estimation approach rather than a selective rejection of false echoes is chosen. Owing to their specific design, these algorithms a particularly good candidates to normalize the data stream before applying processing methods based on ping history. Their performances and their robustness against signal components in the ref- cells are examined. Recommendations about the implementation of the algorithms are derived. The results obtained from applying the algorithms to a real data set show that they perform well.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
AD1120698

Entities

People

  • X. M. De Gastines

Organizations

  • Centre for Maritime Research and Experimentation

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Active Sonar
  • Algorithms
  • Background Noise
  • Data Sets
  • Detection
  • Detectors
  • Estimators
  • False Alarms
  • Filters
  • Filtration
  • Governments
  • Matched Filters
  • Monte Carlo Method
  • Nato
  • New York
  • Noise
  • Order Statistics
  • Random Variables
  • Signal Processing
  • Sonar Signals
  • Statistics

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Computational Modeling and Simulation
  • Neural Network Machine Learning.