LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy
This addresses localization accuracy for batteryless systems, which is an incremental improvement in a domain-specific context.
The paper tackles the problem of sound source localization in batteryless systems with missing data due to energy harvesting, proposing LOCUS to recover corrupted features and achieving up to 36.91% error reduction on datasets and 25.87-59.46% gains in real-world deployments.
Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from missing data due to the stochastic nature of energy harvesting, degrading localization performance. We propose LOCUS, a deep learning framework that recovers corrupted features in such settings. LOCUS integrates three modules: (1) Information-Weighted Focus (InFo) to identify corrupted regions, (2) Latent Feature Synthesizer (LaFS) to reconstruct missing features, and (3) Guided Replacement (GRep) to restore data without altering valid inputs. LOCUS significantly improves DoA accuracy under missing-channel conditions, achieving up to 36.91% error reduction on DCASE and LargeSet, and 25.87-59.46% gains in real-world deployments. We release a 50-hour multichannel dataset to support future research on localization under energy constraints. Our code and data are available at: https://bashlab.github.io/locus_project/