# A Sequential Detection and Tracking of Very Low SNR Objects

arXiv: https://arxiv.org/abs/2312.15823 · HTML: https://mlsanity.com/paper/2312.15823/

## 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.

## Builds on (most cited first, 5 of 5)

- [Fundamentals of Gaussian CM Sequences](https://mlsanity.com/paper/1811.05086/) (2018)
- [Reciprocal Sequences as CM Sequences](https://mlsanity.com/paper/1811.05639/) (2018)
- [CM Sequence based Trajectory Modeling with Destination](https://mlsanity.com/paper/1811.08021/) (2018)
- [Equivalent CM Models](https://mlsanity.com/paper/1910.12374/) (2019)
- [Track Before Detect of Low SNR Objects in a Sequence of Image Frames Using Particle Filter](https://mlsanity.com/paper/2212.13020/) (2022)

## 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.