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

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.

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Correspondence to Samuel J. Davey.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Davey, S.J., Rutten, M.G. & Cheung, B. A Comparison of Detection Performance for Several Track-before-Detect Algorithms. EURASIP J. Adv. Signal Process. 2008, 428036 (2007). https://doi.org/10.1155/2008/428036

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Keywords

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