Skip to content

An overview of maximum-likelihood based algorithms for estimating multipath parameters

Research output: Working paperWorking paper and Preprints

Original languageEnglish
Publication dateMay 2003
Pages10 p
StatePublished

Abstract

Recently the European research trend has shown an increased interest on the use of maximum-likelihood based algorithms, e.g. the Space-Alternating Generalised Expectation-maximisation (SAGE) algorithm, to estimate multipath parameters from raw measurement data. This process can require considerable processing time and resources, especially when dealing with vast multi-dimensional measurement databases. With the aim of achieving significant timesaving, and reducing both the memory utilisation and processing power of computing resources, different versions of maximum-likelihood based algorithms have been developed. This paper provides a general overview of these algorithms based on different implementation methodologies that can achieve the above objectives successfully, subject to some prerequisites. A number of results based on numerical simulations and real measurement data is also presented

Additional information

Contributor: European Cooperation in the Field of Scientific and Technical Research (EURO-COST)

Documents

  • TD-03-090

    Author final version (often known as postprint) , 396 KB, PDF-document

Research areas

  • multipath

View research connections

Related faculties, schools or groups