Adaptive Subspace Classification Technique Using Fuzzy Support Vector Machine for Supspace-based TOA Estimation in a Multipath Channel

Abstract

In this paper, an adaptive subspace classification technique using a fuzzy support vector machine (FSVM) is proposed for subspace-based TOA estimation in a multipath channel. For the subspace-based algorithm to work properly, the signal subspace should be correctly estimated from a data matrix composed of the received samples. Through SVD on the data matrix, singular values and vectors are derived in pairs. According to the derived singular values, the corresponding singular vectors are selected as a set of basis-spanning signal subspaces. Criteria such as the Akaike information criterion (AIC) and minimum description length criterion (MDLC) have previously been adopted for subspace classification. Instead of using these criteria, we propose an adaptive classification technique using a FSVM for improved subspace-based TOA estimation in a multipath channel. © 2022 IEEE.