Hidden Markov Model as a Framework for Situational Awareness

Abstract

In this paper we present a hidden Markov model (HMM) based framework for situational awareness that utilizes multi-sensor multiple modality data. Situational awareness is a process that comes to a conclusion based on the events that take place over a period of time across a wide area. We show that each state in the HMM is an event that leads to a situation and the transition from one state to another is determined based on the probability of detection of certain events using multiple sensors of multiple modalities - thereby using sensor fusion for situational awareness. We show the construction of HMM and apply it to the data collected using a suite of sensors on a Packbot.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2008
Accession Number
ADA520475

Entities

People

  • Thyagaraju Damarla

Organizations

  • United States Army Research Laboratory

Tags

DTIC Thesaurus Topics

  • Acoustic Detectors
  • Algorithms
  • Data Analysis
  • Data Processing
  • Detection
  • Detectors
  • Frequency
  • Hidden Markov Models
  • Infrared Detectors
  • Magnetic Detectors
  • Magnetic Materials
  • Magnetometers
  • Markov Models
  • Models
  • Operating Systems
  • Probability
  • Situational Awareness

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.