Achieving Near-Optimal Sensor Allocation Policies Through Reinforcement Learning.
Abstract
TACTICAL AIRCRAFT MUST FREQUENTLY PERFORM COMPLEX SEQUENTIAL TASKS IN WHICH THEY RELY HEAVILY ON THE INTEGRATION OF SENSORY DATA TO ASSESS STATE AND MAINTAIN SITUATIONAL AWARENESS. IN MODERN SYSTEMS, THE CONTROL OF THE SENSORS' INFORMATION-GATHERING ACTIVITIES IS CRITICAL-OPTIMAL PERFORMANCE IS DESIRED. BUT THIS IS MADE DIFFICULT BY THE REQUIREMENTS TO CONTEND WITH SOPHISTICATED FLEXIBLE SENSORY ASSETS, AND VOLATILE, UNCERTAIN ENVIRONMENTS. THIS PAPER INTRODUCES THE SENSOR MANAGEMENT PROBLEM AND THE PLAUSIBILITY OF LEVERAGING A MACHINE LEARNING ALGORITHM TOWARD THIS DIFFICULT CHALLENGE.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1996
- Accession Number
- ADA318335
Entities
People
- Pinkesh Malhotra
Organizations
- Wright Laboratory