Single-AP indoor localization via multi-directional beamforming

With the increasing demand for accurate indoor localization in smart homes, robotics, and commercial scenarios, achieving precise location using minimal infrastructure has become essential yet challenging. Traditional RSSI-based fingerprinting methods typically require multiple APs, limiting their practicality in single-AP scenarios commonly found in real-world home environments. To overcome this limitation, we propose a single-AP localization method that employs multi-directional beamforming to generate rich RSSI fingerprints. By sweeping the AP beamforming angle, the method captures directional RSSI features, significantly improving localization accuracy. Additionally, we introduce an angular embedding mechanism, explicitly encoding beamforming direction into neural network inputs to enhance robustness, especially under limited labeled data conditions. Extensive simulation and real-world experiments demonstrate that our proposed approach significantly outperforms conventional single-AP RSSI methods, consistently achieving sub-meter accuracy with small training datasets, thereby validating its effectiveness.