Unified Frequency Hopper Detection and Prosecution Methodology, 16-R9544

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Principal Investigator
Matthew B. Grantz

Inclusive Dates:  04/01/05 – Current

Background - Some SwRI clients have expressed interest in developing the ability to reliably detect and process frequency hopping (FH) signals. Existing SwRI capabilities are effective with some specific types of FH transmitters, but the current demand is for more general capabilities and for improvement of the reliability and accuracy of existing systems. This research effort aims to address these problems. We also aim to establish a rigorous foundation for testing, evaluating, and promoting capabilities against specified signals in view of basic system-level specifications.

Approach - The effort has three specific goals to achieve the aims listed above:

  • Development of FH prosecution architecture that unifies the existing techniques and addresses current and future concerns. The goal is to produce a starting point for a lasting division asset that can reduce risk on future projects and facilitate further research.
  • Development of a test bench using generic scientific and data processing software tools. The test bench will (a) help visualize FH-related data, (b) help design and configure prosecution algorithms, and (c) run data files through configured clustering algorithms and collect accuracy statistics. This can be done manually or in batch over a list of files.
  • Development and assessment of new techniques for prosecuting FH signals. This includes the ability to detect the presence and number of transmitters based on a wide variety of characteristic features, such as angle-of-arrival, signal strength, and frequency spacing.

Accomplishments - The project has accomplished the following objectives to date:

  • Designed and implemented a clusterer, bringing together the various approaches used on previous projects.
  • Developed a basic test bench tool using generic scientific and data processing tools for visualization and simulation.
  • Successfully integrated the clusterer and tools into a complete wideband system, demonstrating capability on transmitted signals.
  • Investigated and developed multiple clustering algorithms.
  • Developed statistical models of typical signals and noise, and studied techniques for enhancing our modeling techniques to increase detection accuracy and flexibility.

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