# Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep

arXiv: https://arxiv.org/abs/2109.04360 · HTML: https://mlsanity.com/paper/2109.04360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Gaussian Processes for Regression using Approximate Expectation Propagation](https://mlsanity.com/paper/1602.04133/) (2016)

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

- [Depth-Constrained ASV Navigation with Deep RL and Limited Sensing](https://mlsanity.com/paper/2504.18253/) (2025)

## Abstract (arXiv)

In indoor positioning, signal fluctuation is highly location-dependent. However, signal uncertainty is one critical yet commonly overlooked dimension of the radio signal to be fingerprinted. This paper reviews the commonly used Gaussian Processes (GP) for probabilistic positioning and points out the pitfall of using GP to model signal fingerprint uncertainty. This paper also proposes Deep Gaussian Processes (DGP) as a more informative alternative to address the issue. How DGP better measures uncertainty in signal fingerprinting is evaluated via simulated and realistically collected datasets.