Multiple Target Tracking with Measurements of Uncertain Origin.

Abstract

The problem of estimating the state of a target in the presence of measurements of uncertain origin has received a great deal of attention recently. When this origin question cannot be resolved with certainty, one can only make probabilistic inferences as to which detection originated from the target of interest. The investigation reported here deals with a new class of problems characterized by the following: the correct measurement arrival (detection) times for a target of interest occur according to a stochastic process. Another stochastic process governs the arrival times of the false alarms. Thus detections occur one at a time and while some of them can be discarded as not having originated from the target, the remaining ones cannot be associated with certainty with the target. This problem is motivated by the fact that in some tracking problems detections from the target of interest occurs on an irregular basis. A procedure that associates probabilistically these measurements to the target is developed together with a corresponding estimator. The optimal estimator as well as a number of suboptimal algorithms that are real time implementable are presented together with simulation results. The simulations also indicate that the probabilistic data association filter with time-of-arrival information is significantly superior to the filter which uses only measurement location information for probabilistic data association.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1979
Accession Number
ADA076594

Entities

People

  • G. D. Marcus
  • Y. Bar-shalom

Organizations

  • University of Connecticut

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Cartesian Coordinates
  • Data Association
  • Detection
  • Detectors
  • Estimators
  • False Alarms
  • Kalman Filters
  • Multiple Targets
  • Multitarget Tracking
  • Noise
  • Optimal Estimators
  • Probability
  • Simulations
  • Stochastic Processes
  • Target Tracking
  • Warning Systems

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms