Detection Thresholds for Multi-Target Tracking in Clutter.

Abstract

Tracking performance depends upon the quality of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well-understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the values of the measurement inputs. When the origin of the measurements is also uncertain, one has the widely- studied problem of data association (or data correlation), and tracking performance depends critically on additional parameters, primarily the probabilities of detection and false alarm. In this paper we derive a modified Riccati equation that quantifies (approximately) the dependence of the state error covariance on these parameters. We also show how to use an ROC curve in conjunction with the above relationship to determine an 'optimal' detection threshold in the signal processing system that provides measurements to the tracker. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1981
Accession Number
ADA097188

Entities

People

  • Molly Scheffe
  • Thomas E. Fortmann
  • Yaakov Bar-Shalom

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • C4I
  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computer Science
  • Data Association
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Equations
  • Estimators
  • False Alarms
  • Frequency
  • Military Research
  • Multitarget Tracking
  • Riccati Equation
  • Signal Processing
  • Target Tracking
  • Warning Systems

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Regression Analysis.

Technology Areas

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