Enhanced Multistatic Active Sonar via Innovative Signal Processing

Abstract

Multistatic active sonar systems involve the transmission and reception of one or more probing sequences, which provide a basis for extraction of target information in a region of interest. The probing sequences at the transmitter and signal processing at the receiver play crucial roles in the overall system performance. CAN (cyclic algorithm-new) is employed to synthesize probing sequences with good aperiodic autocorrelation properties. The performance of the CAN sequences is compared with those of pseudo random noise and random phase sequences. Two adaptive receiver designs, namely the iterative adaptive approach (IAA) and the sparse learning via iterative minimization (SLIM) method, are also considered. IAA and SLIM are compared with the conventional matched filter method. The performances of the algorithms are illustrated via numerical examples, which show that CAN, IAA, and SLIM can contribute to the overall performance improvement of the active sonar systems.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 2011
Accession Number
ADA554855

Entities

People

  • Jiantao Li

Organizations

  • University of Florida

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Active Sonar
  • Algorithms
  • Compressed Sensing
  • Data Sets
  • Detection
  • Detectors
  • Filters
  • Information Processing
  • Learning
  • Matched Filters
  • Multiple Input Multiple Output
  • Radial Velocity
  • Signal Processing
  • Simulations
  • Sonar
  • Target Recognition
  • Transmitters

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Radio communications and signal processing.