A Near-Ultrasonic Robust Indoor Localization Method Based on Stacking Ensemble Learning

Recent economic advancements have significantly supported the popularity of indoor positioning systems (IPS) and indoor localization-based services (ILBS). This trend is particularly obvious as global navigation satellite systems (GNSS) are ineffective in indoor environments. Traditional IPS, such as WIFI and Bluetooth positioning, face challenges like low accuracy and are prone to non-line-of-sight (NLOS) and noise interference. In response to this issue, we propose a novel near-ultrasonic indoor localization method based on the stacking ensemble model. Initially, the method employs an optimized enhanced cross-correlation technique to effectively mitigate multipath interference in acoustic ranging. Compared to the conventional methods based on peak extraction or fixed thresholding, this approach significantly improves ranging accuracy in reverberant environments. Subsequently, time difference of arrival (TDOA) is extracted as a feature. Finally, we utilized a stacking ensemble learning model, incorporating optimized machine learning models, to train a pre-set dataset. This method, integrating the extracted feature, enables to achieve correct localization results in NLOS and large ranging error. Numerical simulations, ray-tracing acoustic analyses, and empirical validations suggest that our approach notably mitigates errors prevalent in NLOS and acoustically noisy indoor environments, yielding localization accuracy significantly exceeding current methods by 50%-90%.