Skip to content

Advertisement

  • Research Article
  • Open Access

A Comparison of Detection Performance for Several Track-before-Detect Algorithms

EURASIP Journal on Advances in Signal Processing20072008:428036

https://doi.org/10.1155/2008/428036

  • Received: 30 March 2007
  • Accepted: 8 October 2007
  • Published:

Abstract

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.

Keywords

  • Dynamic Programming
  • Point Measurement
  • Quantum Information
  • Detection Algorithm
  • Sensor Data

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, P.O. Box 1500, Edinburgh, SA, 5111, Australia

Copyright

Advertisement