2012Unpublished venueOpen access

Empirical Modeling of Intra-BAN Ranging Errors Based on IR-UWB TOA Estimation

Jihad Hamie, Benoît Denis, Raffaele D'ERRICO, Cédric Richard

Open full text 4 citations

Abstract

In this paper we present a model accounting for dynamic intra-Wireless Body Area Network (WBAN) ranging errors based on Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation in the [3.1, 5.1]GHz and [3.75, 4.25]GHz frequency bands. The latter is compliant with one mandatory band i

Open-access reader

About this research paper

What this paper is about

In this paper we present a model accounting for dynamic intra-Wireless Body Area Network (WBAN) ranging errors based on Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation in the [3.1, 5.1]GHz and [3.75, 4.25]GHz frequency bands. The latter is compliant with one mandatory band i

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In this paper we present a model accounting for dynamic intra-Wireless Body Area Network (WBAN) ranging errors based on Impulse Radio - Ultra Wideband (IR-UWB) Time Of Arrival (TOA) estimation in the [3.1, 5.1]GHz and [3.75, 4.25]GHz frequency bands. The latter is compliant with one mandatory band i

Key concepts: Ranging, Ultra-wideband, Impulse radio, Body area network, Computer science, Wireless, Time of arrival, Radio spectrum

Related papers

Back to paper searchBrowse research topicsOriginal source
Empirical Modeling of Intra-BAN Ranging Errors Based on IR-UWB TOA Estimation — Research Paper | ScholarLens