Testbed Creation to Study Noise Radar Network Weighting Models and Data Fusion with Radio Tomographic Imaging

Abstract

Significant research has been conducted on RTI weighting models; however, very little comparative research has been conducted for NRN weighting methods. In order to create comparative weighting methods for NRN, it is necessary to create a testbed which allows for RTI and NRN research to be conducted simultaneously and allow for data fusion methods to also be researched. After creating the testbed and analyzing results, the newly proposed weighting method provides an up to 33% performance increase in target localization accuracy when compared to the previous weighting model used for NRN. The attenuation image resolution improvements resulted in a 79% performance increase in target localization accuracy for the MAP estimate. In addition to the performance increase, the newly proposed weighting method has the capability to provide a foundation for future research into NRN weighting methods. The testbed created allows for seamless interchanging of data sets, weighting models, and experimental conditions.

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Document Details

Document Type
Technical Report
Publication Date
Mar 24, 2022
Accession Number
AD1166863

Entities

People

  • Ryan M Jans

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Data Fusion
  • Data Mining
  • Data Sets
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Governments
  • Information Processing
  • Information Science
  • Literature Surveys
  • Measurement
  • Sensor Networks
  • Signal Processing
  • Simulations
  • Traumatic Stress Disorder
  • United States Government
  • Wearable Technology
  • Wireless Sensor Networks

Readers

  • Distributed Systems and Data Platform Development
  • Radar Systems Engineering.