OODT: LOS Signal Identification for Acoustic Indoor Localization From Stream Perspective

Acoustic-based technologies have attracted more attention in indoor ranging-based localization and tracking applications. To achieve high-accuracy ranging measurements, the precise identification of line-of-sight (LOS) acoustic signals is essential. This article proposes a novel off-online dynamic training (OODT) method to identify LOS acoustic signals from stream perspective based on a few training data in dynamic indoor environment. The dynamic online training method is proposed to identify the unlabeled acoustic feature stream by the parent–child model, utilizing the dynamic prior probability from time series information and a selection strategy based on the prediction risk. Then, the trustworthy pseudo-labeled streaming samples are accumulated into the child-models by online learning in real time. To reduce the impact of discarding the untrustworthy LOS signals, the off-online retraining method is proposed to incorporate the spatial information into the parent-models, which uses the category distribution of all pseudo-labeled streaming samples as the prior probability for iterative retraining. Subsequently, a new round of the dynamic online training is conducted to update the pseudo-labels of feature stream. Experiments demonstrate that the dynamic online training method has a higher identification precision of LOS acoustic signals from stream perspective, reaching 98% and more than 93.06% in above-ground and underground experimental scenarios. Moreover, the adaption for the concept drift is also verified with identification precision of 97.98% and 98.23%, respectively. The off-online retraining method further optimize the performance of the parent–child models in a single scenario. In conclusion, the proposed OODT method can autonomously identify dynamic acoustic signals with strong robustness and scenario drift adaptability.