One-vs-One Multiclass Least Squares Support Vector Machines for Direction of Arrival Estimation

摘要

This paper presents a multiclass, multilabel implementation of Least Squares Support Vector Machines (LS-SVM) for DOA estimation in a CDMA system. For any estimation or classification system the algorithm's capabilities and performance must be evaluated. This paper includes a vast ensemble of data supporting the machine learning based DOA estimation algorithm. Accurate performance characterization of the algorithm is required to justify the results and prove that multiclass machine learning methods can be successfully applied to wireless communication problems. Thel earning algorithm presented in this paper includes steps for generating statistics on the multiclass evaluation path. The error statistics provide a confidence level of the classification accuracy.