On Mutual Information Neural Estimation for Localization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909881508823040 |
|---|---|
| author | Hinderer, Sven Buchfink, Manuel Yang, Bin |
| author_facet | Hinderer, Sven Buchfink, Manuel Yang, Bin |
| contents | Mutual information (MI) is a promising candidate measure for the assessment and optimization of localization systems, as it captures nonlinear dependencies between random variables. However, the high cost of computing MI, especially for high-dimensional problems, prohibits its application for many real-world localization systems. We evaluate an algorithm from a new class of neural MI estimators called Mutual Information Neural Estimation (MINE) to approximate the MI between the set of feasible user element (UE) locations and the corresponding set of measurements from said UE locations used for positioning. We apply this estimator to a simulated multilateration (MLAT) system, where the true MI for benchmarking can be approximated by Monte Carlo simulation. The estimator is experimentally evaluated w.r.t. its convergence and consistency and we investigate the usefulness of MI for assessing simple MLAT systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Mutual Information Neural Estimation for Localization Hinderer, Sven Buchfink, Manuel Yang, Bin Signal Processing Mutual information (MI) is a promising candidate measure for the assessment and optimization of localization systems, as it captures nonlinear dependencies between random variables. However, the high cost of computing MI, especially for high-dimensional problems, prohibits its application for many real-world localization systems. We evaluate an algorithm from a new class of neural MI estimators called Mutual Information Neural Estimation (MINE) to approximate the MI between the set of feasible user element (UE) locations and the corresponding set of measurements from said UE locations used for positioning. We apply this estimator to a simulated multilateration (MLAT) system, where the true MI for benchmarking can be approximated by Monte Carlo simulation. The estimator is experimentally evaluated w.r.t. its convergence and consistency and we investigate the usefulness of MI for assessing simple MLAT systems. |
| title | On Mutual Information Neural Estimation for Localization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.17344 |