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<p>In large-scale multi-building and multi-floor environments, fingerprint-based indoor localization is challenged by environmental non-stationarity, device heterogeneity, and the cost of maintaining separate models per building and floor. Existing deep methods either rely on unstable three-dimensional coordinate regression or adopt cascaded pipelines that first classify building/floor and then perform local regression, which amplifies upstream errors and increases latency and energy consumption. We propose Multi-Grid Hypothesis Learning (MGHL), a unified framework that performs building-floor recognition and continuous position estimation within a single backbone and a single forward pass. MGHL discretizes the global 2D space into grid cells and uses distance-aware multi-label supervision to construct multiple spatial hypotheses. A robust training objective combining soft-label cross-entropy, focal loss, and label smoothing improves probability calibration and tail performance, while lightweight random masking of access-point (AP) features during training simulates AP failures and temporal drift without adding inference cost. MGHL adopts a shared-embedding multi-head architecture to jointly model building, floor, and grid-level outputs, with a masking mechanism restricting hypotheses to the predicted building-floor subset. Experiments on three benchmark datasets, namely UJIIndoorLoc, SODIndoorLoc, and UTSIndoorLoc, under the EvAAL protocol show that MGHL consistently improves joint building-floor-position accuracy and mean localization error over strong single-model baselines, while approaching the overall performance of traditional multi-model systems.</p>

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MGHL: Multi-Grid Hypothesis Learning for Unified Multi-Building and Multi-Floor Indoor Localization

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Shiting Feng,
  • Shiyan Li,
  • Yiying Yan,
  • Qinglin Liu,
  • Jianpeng Guo

摘要

In large-scale multi-building and multi-floor environments, fingerprint-based indoor localization is challenged by environmental non-stationarity, device heterogeneity, and the cost of maintaining separate models per building and floor. Existing deep methods either rely on unstable three-dimensional coordinate regression or adopt cascaded pipelines that first classify building/floor and then perform local regression, which amplifies upstream errors and increases latency and energy consumption. We propose Multi-Grid Hypothesis Learning (MGHL), a unified framework that performs building-floor recognition and continuous position estimation within a single backbone and a single forward pass. MGHL discretizes the global 2D space into grid cells and uses distance-aware multi-label supervision to construct multiple spatial hypotheses. A robust training objective combining soft-label cross-entropy, focal loss, and label smoothing improves probability calibration and tail performance, while lightweight random masking of access-point (AP) features during training simulates AP failures and temporal drift without adding inference cost. MGHL adopts a shared-embedding multi-head architecture to jointly model building, floor, and grid-level outputs, with a masking mechanism restricting hypotheses to the predicted building-floor subset. Experiments on three benchmark datasets, namely UJIIndoorLoc, SODIndoorLoc, and UTSIndoorLoc, under the EvAAL protocol show that MGHL consistently improves joint building-floor-position accuracy and mean localization error over strong single-model baselines, while approaching the overall performance of traditional multi-model systems.