Target localization, identification and sensing using latent symmetries
This work introduces a new sensing paradigm for target localization and identification in 3D open systems, but the approach is domain-specific and currently demonstrated only on capacitance matrix models.
The authors demonstrate that arrays of scatterers with latent symmetries can be used as sensors to localize and identify intruders by analyzing symmetry breaking. Bayesian inference and neural networks outperform dictionary-based methods under noise.
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.