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

Abstract

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. The learning 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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADP014209

Entities

People

  • Chaouki T. Abdallah
  • Judd A. Rohwer

Organizations

  • Sandia National Laboratories

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Angle Of Arrival
  • Antenna Arrays
  • Communication Channels
  • Communication Systems
  • Computational Complexity
  • Computers
  • Data Mining
  • Electrical Engineering
  • Information Processing
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Multiple Access
  • Neural Networks
  • Signal Processing
  • Supervised Machine Learning

Fields of Study

  • Computer science
  • Engineering

Readers

  • Neural Network Machine Learning.
  • Phased Array Antenna Design.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference