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.