LOCUS：具有信道不确定性与间歇供能的定位
LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy
AI总结：
针对无电池系统能量采集随机性导致的多通道数据缺失问题，本文提出深度学习框架LOCUS，通过InFo、LaFS和GRep三个模块识别并重建受损特征，显著提升DoA定位精度，并发布50小时多通道数据集。
摘要：
精确的声源定位（SSL），如到达方向（DoA）估计，依赖于一致的多通道数据。然而，无电池系统由于能量采集的随机性，常常遭受数据缺失的困扰，从而降低定位性能。我们提出LOCUS，一个深度学习框架，用于在此类场景下恢复受损特征。LOCUS集成三个模块：（1）信息加权聚焦（InFo）用于识别受损区域，（2）潜在特征合成器（LaFS）用于重建缺失特征，（3）引导替换（GRep）用于在不改变有效输入的情况下恢复数据。LOCUS在通道缺失条件下显著提高了DoA精度，在DCASE和LargeSet上实现了最高36.91%的误差降低，并在实际部署中获得了25.87%至59.46%的增益。我们发布了一个50小时的多通道数据集，以支持未来在能量约束下定位的研究。我们的代码和数据可在以下网址获取：https://bashlab.github.io/locus_project/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
