Data Compression Trade-Offs for TDOA/FDOA Geo-Location Systems

Abstract

The research results focus on new data compression insights and methods that can enable the sharing of data for enhanced geo-location of RF emitters. The work was focused in four areas: (1) New Theoretical Results: Data compression ideas were applied to the issue of how to select and configure a set of available sensors for location processing. This proved to be a challenging task. (2) Refine & Extend Previous Results: The short-time Fourier transform (STFT) was integrated into the data compression algorithm and was shown to properly operate. (3) Integrate Into a Matlab-based Test-Bed: Matlab routines for data compression were developed and integrated into a single Matlab application. (4) General Location Studies: It was shown that there are issues in using previous results that were developed explicitly for sonar signal cases when the signal was modeled as wide-sense stationary Gaussian process. Results are provided for signal models suitable for the communication signal case.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2008
Accession Number
ADA483829

Entities

People

  • Mark L. Fowler

Organizations

  • Binghamton University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Acoustic Signals
  • Air Force Research Laboratories
  • Algorithms
  • Data Compression
  • Data Science
  • Detection
  • Gaussian Processes
  • Information Processing
  • Information Science
  • Information Theory
  • Probability
  • Sensor Networks
  • Signal Processing
  • Sonar Signals
  • Statistical Algorithms
  • Test Beds
  • Unmanned Aerial Vehicles

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Sensor Fusion and Tracking Systems.