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Accurate sound source localization (SSL) requires consistent multichanneldata for reliable degree of arrival (DoA) estimation. However, intermittentlypowered batteryless systems often suffer from incomplete sensor data due to thestochastic nature of energy harvesting. Existing methods struggle with missingchannels, leading to significant performance degradation. In this paper, wepropose $\textit{LOCUS}$, a novel deep learning-based system designed torecover corrupted features for SSL in batteryless systems. $\textit{LOCUS}$addresses missing data by leveraging information entropy estimation andconditional interpolation, combining three modules: (1) Information-WeightedFocus (InFo), which identifies and quantifies corrupted data elements, (2)Latent Feature Synthesizer (LaFS), which synthesizes missing features, and (3)Guided Replacement (GRep), which intelligently replaces missing elements whilepreserving valid data. We demonstrate significant performance improvementsusing two datasets: DCASE and LargeSet, where $\textit{LOCUS}$ achieves up to$36.91\%$ lower DoA error compared to existing methods. Real-world evaluationsacross three environments with intermittent power sources show a$25.87-59.46\%$ improvement in performance when channels are stochasticallymissing. Additionally, we release a 50-hour multichannel dataset to supportfurther research in SSL.

LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy