A Hybrid Computational Framework for Regional Classification in Gravity-Aided Navigation: Integrating Hierarchical Clustering and Random Forests

Data-driven gravity-aided underwater navigation requires reliable region-of-acceptance (RoA) classification to ensure robust localization. We formulate RoA prediction on geospatial gravity-anomaly fields as a pattern-recognition task and design a hybrid computational pipeline that couples unsupervised calibration with supervised inference. This paper proposes: (i) feature normalization over longitude–latitude–anomaly triplets; (ii) Ward hierarchical clustering to calibrate RoA labels; and (iii) a random-forest classifier trained on the calibrated labels, benchmarked against SVM, KNN, and decision-tree baselines. The trained model is directly transferred to a second dataset to assess cross-dataset generalization. Experiments show high accuracy and balanced precision/recall on held-out data, and 0.99 accuracy in the transfer evaluation, while maintaining low inference latency suitable for deployment. The framework offers a practical, generalizable solution for RoA prediction that can be integrated with downstream path-planning and decision modules.