mlsanityData to 14 Oct 2026

A Sequential Detection and Tracking of Very Low SNR Objects

arXiv 2312.15823 ↗First posted 25 Dec 2023eess.SY cs.AI cs.HCcited by 0

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Abstract (arXiv)

A sequential detection and tracking (SDT) approach is proposed for detection and tracking of very low signal-to-noise (SNR) objects. The proposed approach is compared with two existing particle filter track-before-track (TBD) methods. It is shown that the former outperforms the latter. A conventional detection and tracking (CDT) approach, based on one-data-frame thresholding, is considered as a benchmark for comparison. Simulations demonstrate the performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 32 references that are in the map. First posted on arXiv 25 Dec 2023.

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5 of 32 references.

References · 5

  1. 2018Fundamentals of Gaussian CM Sequencescited by 1
  2. 2018Reciprocal Sequences as CM Sequencescited by 1
  3. 2018CM Sequence based Trajectory Modeling with Destinationcited by 1
  4. 2019Equivalent CM Modelscited by 1
  5. 2022Track Before Detect of Low SNR Objects in a Sequence of Image Frames Using Particle Filtercited by 1

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