An ML-MHT Approach to Tracking Dim Targets in Large Sensor Networks

Abstract

Poor individual sensor performance as well as a large number of sensor scans per time interval are two challenges for multi-target tracking is large sensor networks. We introduce a two-stage processing scheme (ML-MHT) to address the former issue, and another to address the latter issue (MHT2). We consider as well the combination of these two techniques (ML-MHT2). Simulation results are encouraging. Future work will include application of these techniques to more challenging multi-sensor datasets characterized by extremely poor detection and localization performance.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2010
Accession Number
ADA564338

Entities

People

  • Craig Carthel
  • Stefano Coraluppi

Organizations

  • Centre for Maritime Research and Experimentation

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Data Association
  • Data Reduction
  • Detection
  • Detectors
  • Intervals
  • Military Research
  • Multiple Hypothesis Tracking
  • Multistatic Tracking
  • Multitarget Tracking
  • Networks
  • Sensor Networks
  • Simulations
  • Target Tracking
  • Time Intervals

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.