# Extremum-Based Joint Compression and Detection for Distributed Sensing

arXiv: https://arxiv.org/abs/2603.27733 · HTML: https://mlsanity.com/paper/2603.27733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Joint Extremum Compression and Detection of a Time-Delayed Signal for Distributed Sensing](https://mlsanity.com/paper/2607.10874/) (2026)

## Abstract (arXiv)

We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a k-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a k-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations.