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

Abstract

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 paper proposes a novel Off-Online Dynamic Training method(OODT) 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.