Device-free localization using empirical wavelet transform-based extreme learning machine

Abstract

Listen

Translate article

Device-Free Localization (DFL) is becoming one of the new trends in the localization field and has attracted more attentions, due to its advantage that the target to be localized does not need to be attached to any electronic device. In our previous work, we have applied extreme learning machine (ELM) to DFL combining parameterized geometrical feature extraction (PGFE) for extending the input feature of ELM. Although, the proposed PGFE-ELM for DFL is more robust and can obtain better performance, we believe that the localization accuracy of ELM-based DFL can be improved further, due to the dynamic and uncertain signal propagation environment in the monitoring area. Thus, we will propose a novel ELM based on empirical wavelet transform (EWT), named EWT-ELM, to improve the accuracy and efficiency of DFL. EWT-ELM consists of two components, i.e., the prediction model and the residual model. The prediction model is used for building the feature mapping of the input and the output through an ELM. In the residual model, the residual of the prediction model is firstly decomposed into some modes by EWT for alleviating the negative impacts of its nonlinear characteristic, and then, the decomposed modes will be modeled through the corresponding ELMs. In addition, the proposed EWT-ELM for DFL is trained in the offline training phase and performed for real-time estimation of target's location in the online localization phase. Experimental results show that the proposed EWT-ELM can improve the localization accuracy significantly comparing with the classic ELM.