Comparison of Techniques for Ground Target Tracking

Abstract

There have been a lot of studies addressing target-tracking problems, in which targets like aircraft and missiles can move freely in the air without hard spatial constraints. Tracking ground targets is a completely different case. Variable terrain structures not only limit the target's moving capability, but also degrade the quality of measurement data. This paper describes an exploratory research project which studied the tracking of a single ground target via traditional and atypical approaches. Traditional Kalman techniques taking into account the additional information provided by the ground restrictions in the tracking process, a road network in our study, were implemented. Additionally, another tracker using the Hidden Markov Model (HMM) with transition array was also developed under the same scenario. The results showed that Kalman techniques with available road information significantly outperform the conventional Kalman approaches in terms of longitudinal and transversal errors at the time when the target maneuvers. The proposed adaptive HMM tracker, composed of some regional HMM trackers, is not sensitive to transversal maneuvers, but may yield large longitudinal errors at the time when the target approaches the boundary of each subscenario.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADA400079

Entities

People

  • Chih-chung Ke
  • James Llinas
  • Jesus G. Herrero

Organizations

  • University at Buffalo

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Detectors
  • Estimators
  • Geographic Regions
  • Hidden Markov Models
  • Kalman Filters
  • Maneuvers
  • Markov Models
  • Measurement
  • Models
  • Observation
  • Probability
  • Probability Distributions
  • Statistical Algorithms
  • Target Tracking
  • Transitions

Fields of Study

  • Engineering

Readers

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