Rigid Neighborhood Discovery and Decentralized Localization for Multi-Agent Mobile Networks

Abstract

Location awareness is crucial for many mobile-network applications. While commercial applications rely heavily on the convenience and ubiquity of GPS, military applications must remain robust across the spectrum of denied and contested battlespaces. The use of interagent RF ranging measurements provides one means of reconstructing the relative network geometry. If all pairwise range measurements are always available to all agents, each agent can then separately solve for the network geometry. For dynamic mobile networks with constrained communications, and particularly for extended networks with many agents, the available range measurements may not uniquely specify the entire network geometry. Instead, each agent must discover and localize a solvable subset of the network. This report presents a decentralized method of rigid-neighborhood discovery and localization. The method is implemented in simulation under conditions of range-limited measurement and communication. Results suggest that rigid-neighborhood selection can improve relative localization compared to full-network or random-neighborhood selection.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2020
Accession Number
AD1098063

Entities

People

  • Moshe Hamaoui

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Aircrafts
  • Algorithms
  • Buildings And Structures
  • Cellular Networks
  • Detectors
  • Geometry
  • Global Positioning Systems
  • Graph Theory
  • Graphs
  • Guarantees
  • Information Processing
  • Kalman Filters
  • Mathematical Analysis
  • Mathematics
  • Measurement
  • Military Applications
  • Military Research
  • Misalignment
  • Mobile Phones
  • Networks
  • Rigidity
  • Simulations
  • Spectra
  • Topology
  • Trajectories
  • Two Dimensional

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Operations Research
  • Radar Systems Engineering.

Technology Areas

  • Space