# Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

arXiv: https://arxiv.org/abs/2211.00806 · HTML: https://mlsanity.com/paper/2211.00806/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Passive Sensing and Communication Using Visible Light: Taxonomy, Challenges and Opportunities](https://mlsanity.com/paper/1704.01331/) (2017)
- [Artificial Neural Network for LiDAL Systems](https://mlsanity.com/paper/1904.12687/) (2019)

## Abstract (arXiv)

Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy.