A Novel Multi-Scale Dual-Modality CNN Approach Leveraging Light and Dark Fingerprints for RSS-Based Indoor Positioning

Abstract

Traditional indoor localization using received signal strength (RSS) fingerprinting commonly uses measured RSS values as location-related inputs. This paper investigates a paired light–dark representation of an RSS fingerprint image and a cross-modality multi-scale fusion model for small-scale indoor localization. Specifically, each RSS fingerprint image is inverted in pixel intensity, and the original and inverted images are retained as synchronized representations of the same RSS measurement rather than as independent physical modalities or separate augmented samples. The paired images are processed by two parallel convolutional neural network (CNN) paths combined with a cross-modality fusion (CMF) module, which extracts and fuses multi-scale feature representations. The fused features are fed into fully connected layers to predict the coordinate vector. Experimental comparisons with traditional and advanced methods on four public datasets show positioning errors comparable to the strongest advanced benchmark and lower errors than the considered traditional baselines. The proposed model uses approximately 30.8% fewer parameters than the advanced benchmark.