Toward Generalizable Ranging Error Mitigation: A Multi-Source Domain Adaptation Approach

Ranging error mitigation is key to improving indoor localization accuracy, but diverse environments challenge algorithm generalization. This letter proposes a multi-source domain adaptation framework for ranging error mitigation, where a weighted maximum mean discrepancy (MMD) is design to dynamically balances domain similarity and regression accuracy during feature alignment. By dynamically aligning domain mappings, the proposed method reduces negative transfer effects caused by varying discrepancies between different source and target domains. Experimental results show that the proposed network outperforms existing deep learning approaches in reducing ranging error across diverse indoor scenarios.