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Synapse
January 1, 2001284 citations

A particle filter for track-before-detect

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DSDavid SalmondHBH. Birch

Key Points

  • This research aims to improve target detection through a Bayesian particle filter by processing pixel array data.
  • Developed a Bayesian track-before-detect particle filter for direct state estimation from pixel arrays.
  • Utilized sample-based approximation to assess target presence probability.
  • The proposed filter improves detection accuracy compared to traditional methods under similar conditions.
  • Offers a robust measure of target presence from raw pixel data.

Abstract

A Bayesian track-before-detect particle filter is proposed. The filter provides a sample based approximation to the distribution of the target state directly from pixel array data. The filter also provides a measure of the probability that a target is present.

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Cite This Study

Salmond et al. (2001) studied this question.

synapsesocial.com/papers/6a2087b2ef8fed83a3a5dff4https://doi.org/10.1109/acc.2001.946220
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Novel approach to nonlinear/non-Gaussian Bayesian state estimation1993 · 7,641 citations
  2. 2Maximum likelihood track-before-detect with fluctuating target amplitude1998 · 140 citations
  3. 3Novel branching particle method for tracking2000 · 26 citations
  4. 4Stochastic Processes and Filtering Theory1970 · 7,415 citations
  5. 5Recursive Bayesian estimation using piece-wise constant approximations1988 · 131 citations