# Target localization, identification and sensing using latent symmetries

arXiv: https://arxiv.org/abs/2606.01421 · HTML: https://mlsanity.com/paper/2606.01421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Imaging with Equivariant Deep Learning](https://mlsanity.com/paper/2209.01725/) (2022)
- [Gaussian processes for Bayesian inverse problems associated with linear partial differential equations](https://mlsanity.com/paper/2307.08343/) (2023)
- [Bioinspired random projections for robust, sparse classification](https://mlsanity.com/paper/2206.09222/) (2022)
- [Hidden Symmetries in Real and Theoretical Networks](https://mlsanity.com/paper/1803.02328/) (2018)
- [Covert Scattering Control in Metamaterials with Non-Locally Encoded Hidden Symmetry](https://mlsanity.com/paper/2305.00906/) (2023)
- [Morphogenesis of sound creates acoustic rainbows](https://mlsanity.com/paper/2408.14953/) (2024)

## Abstract (arXiv)

We show that an array of scatterers which has been designed to have latent ("hidden") symmetries can be used as a sensor. We use the capacitance matrix as a canonical model for three-dimensional hybridisation and study how the introduction of an "intruder'' scatterer breaks the latent symmetries. By analysing the degree to which each symmetry is broken, we identify the radius of the intruder and localize its position. This can be achieved using a dictionary-based approach, however Bayesian inference or an artificial neural network (multi-layer perceptron) perform better in the presence of measurement noise. To our knowledge, this is the first time latent symmetries have been exploited successfully for sensing problems. It is also the first time latent symmetries have been observed in a three-dimensional open system that cannot be approximated by a sparse graph.