A Quality-Driven Geomagnetic Measurement Framework for Indoor Positioning: Fingerprint Distinguishability Analysis and Robust Trajectory Matching

Indoor geomagnetic positioning utilizes the pervasive magnetic field as a natural signal source for navigation. However, the nonuniqueness of magnetic signatures and environmental interference introduces significant measurement ambiguity, hindering high-precision instrumentation in complex environments. Furthermore, the lack of quantitative metrics for assessing the “localizability” of a magnetic map limits the reliability of existing positioning systems. To address these measurement challenges, this article proposes a quality-driven positioning framework that integrates a robust signal matching strategy with a novel fingerprint quality evaluation system. First, a dual-index evaluation system comprising a discrimination index (DI) and a variation index (VI) is established to quantitatively characterize the spatial distinguishability and temporal consistency of the magnetic field data. This system demonstrates a strong coefficient of determination (R²=0.91) with the mean overall positioning error (MOPE), providing a theoretical basis for predicting measurement uncertainty. Second, to mitigate signal mismatches in repetitive environments, we introduce a modulus-weighted constrained derivative dynamic time warping (CDDTW) method. This method incorporates physical constraints of the magnetic modulus into the matching process to enhance coarse-level signal retrieval. Subsequently, a Multiscale BiGRU-Attention Siamese Network is employed as a fine-grained metric learning model to extract robust features from noisy sensor data. Experimental results indicate that the proposed framework achieves a localization accuracy of better than 3 m. Compared to traditional baseline methods, the proposed approach improves accuracy by over 35% and significantly reduces large error outliers, validating its effectiveness in enhancing the robustness and reliability of geomagnetic measurement systems.