Spatio-Temporal Constrained Refined Nearest Neighbor Fingerprinting Localization

ObjectiveIndoor fingerprint-based localization faces three key challenges. First, Dimensionality Reduction (DR), used to reduce storage and computational costs, often disrupts the geometric correlation between signal features and physical space, which reduces mapping accuracy. Second, signal features present temporal variability caused by human movement or environmental changes. During online mapping, this variability introduces bias and distorts similarity between target and reference points in the low-dimensional space. Third, pseudo-neighbor interference persists because environmental noise or imperfect similarity metrics lead to inaccurate neighbor selection and skew position estimates. To address these issues, this study proposes a Spatio-Temporal Constrained Refined Nearest Neighbor (STC-RNL) fingerprinting localization algorithm designed to provide robust, high-accuracy localization under complex interference conditions.MethodsIn the offline phase, a robust DR framework is constructed by integrating two constraints into a MultiDimensional Scaling (MDS) model. A spatial correlation constraint uses physical distances between reference points and assigns stronger associations to proximate locations to preserve alignment between low-dimensional features and the real layout. A temporal consistency constraint clusters multiple temporal signal samples from the same location into a compact region to suppress feature drift. These constraints, combined with the MDS structure-preserving loss, form the optimization objective, from which low-dimensional features and an explicit mapping matrix are obtained. In the online phase, a progressive refinement mechanism is applied. An initial candidate set is selected using a Euclidean distance threshold. A hybrid similarity metric is then constructed by enhancing shared-neighbor similarity with a Sigmoid-based strategy, which truncates low and smooths high similarities, and fusing it with Euclidean distance to improve discrimination of true neighbors. Subsequently, an iterative Z-score-based filtering procedure removes reference points that deviate from local group characteristics in feature and coordinate domains. The final position is estimated through a similarity-weighted average over the refined neighbor set, assigning higher weights to more reliable references.Results and DiscussionsThe performance of STC-RNL is assessed on a private ITEC dataset and a public SYL dataset. The spatio-temporal constraints enhance the robustness of the mapping matrix under noisy conditions (Table 2). Compared with baseline DR methods, the proposed module reduces mean localization error by at least 6.30% in high-noise scenarios (Fig. 9). In the localization stage, the refined neighbor selection reduces pseudo-neighbor interference. On the ITEC dataset, STC-RNL achieves an average error of 0.959 m, improving performance by 9.61% to 33.68% compared with SSA-XGBoost and SPSO (Table 1). End-to-end comparisons show that STC-RNL reduces the average error by at least 12.42% on ITEC and by at least 7.08% on SYL (Table 2), and its CDF curves demonstrate faster convergence and higher precision, especially within the 1.2 m range (Fig. 10). These results indicate that the algorithm maintains high stability and accuracy with a lower maximum error across datasets.ConclusionsThe STC-RNL algorithm addresses structural distortion and mapping bias found in traditional DR-based localization. By jointly optimizing offline feature embedding with spatio-temporal constraints and online neighbor selection with progressive refinement, the coupling between signal features and physical coordinates is strengthened. The main innovation lies in a synergistic framework that ensures only high-confidence neighbors contribute to the final estimate, improving accuracy and robustness in dynamic environments. Experiments show that the model reduces average localization error by 12.42%～32.80% on ITEC and by 7.08%～13.67% on SYL relative to baseline algorithms, while achieving faster error convergence. Future research may incorporate nonlinear manifold modeling to further improve performance in heterogeneous access point environments.