Decentralized Bayesian Multi Agent Multi Target Search, Localization and Tracking

Abstract

We propose to develop new high performance algorithms for unsolved problems in target search, tracking and localization. We consider both centralized and decentralized settings, with multiple airborne mobile sensors and multiple stochastic moving targets. We focus on resource allocation and path planning to best learn target locations in settings where uncertainty plays a prominent role. How should sensors allocate bandwidth and battery resources toward communication and movement? How should sensors position themselves in light of incoming information about the target and other sensors? We plan to solve these problems within a Bayesian team decision framework using tractable computational methods that are robust to uncertainty about system dynamics and targets’ behavior.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 14, 2022
Source ID
FA95501910283

Entities

People

  • Peter Frazier

Organizations

  • Air Force Office of Scientific Research
  • Cornell University
  • United States Air Force

Tags

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms