Publications de John Farserotu

FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks

Christian Enz, John Farserotu, Vladimir Kopta

The Frequency Modulated Ultra-Wideband (FM-UWB) is known as a low-power, low-complexity modulation scheme targeting low to moderate data rates in applications such as wireless body area networks. In this paper, a thorough review of all FM-UWB receivers and ...

2017

Efficient through-waveguide wireless power transfer for body area networks

Christian Enz, John Farserotu, Vladimir Kopta

2017

Indoor Navigation Research with the Khepera III Mobile Robot: An Experimental Baseline with a Case-study on Ultra-wideband Positioning

Alcherio Martinoli, John Farserotu, Amanda Stella Markowska Prorok, Adrian Arfire, Alexander Bahr

Recent substantial progress in the domain of indoor positioning systems and a growing number of indoor location-based applications are creating the need for systematic, efficient, and precise experimental methods able to assess the localization and perhaps ...

2010

Impulse Radio Ultra-Wideband Ranging Based on Maximum Likelihood Estimation

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We propose a high-resolution ranging algorithm for impulse radio (IR) ultra-WideBand (UWB) communication systems in additive white Gaussian noise. We formulate the ranging problem as a maximum- likelihood (ML) estimation problem for the channel delays and ...

Institute of Electrical and Electronics Engineers2009

Novel Algorithms on Impulse Radio Ultra-WideBand Ranging

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of ranging with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths propagation environments and additive Gaussian noise. We propose a Bayesian detection algorithm where the prior distribution of the channel follows ...

IEEE2009

One Step Impulse Radio Ultra-WideBand Positioning Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of positioning estimation with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths and additive Gaussian noise environments. Most popular positioning algorithms first estimate certain parameters (such as time of arr ...

IEEE2009

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...

IEEE2009

Impulse Radio Ultra-Wideband Ranging Algorithm under Multi User Environments

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

Practical impulse radio ultra-wideband (IR-UWB) ranging systems always have to work in multi-user and weak non-line-of-sight (NLOS) environments. In this paper, we derive a novel IR-UWB ranging estimator under multi-user and weak NLOS environments. We mode ...

IEEE2009

Statistical Analysis of Impulse Radio Ultra-Wideband Multi User Interference based on Measurements

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

Multiuser interference (MUI) statistical models for impulse radio ultra-wideband (IR-UWB) systems can be an important in providing an accurate estimate of the channel state. As such, it can have a major impact on the overall system performance. In the lite ...

IEEE2008

High-Resolution Impulse Radio Ultra Wideband Ranging

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We propose a high resolution ranging algorithm for unsynchronized impulse radio Ultra-wideband (UWB) communication systems in gaussian noise. We pose the ranging problem as a Maximum Likelihood (ML) estimation problem for the channel delays and attenuation ...

2007

A Novel Maximum Likelihood Estimation Of Superimposed Exponential Signals In Noise And Ultra-Wideband Application

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We pose the estimation of the parameters of multiple superimposed exponential signals in additive Gaussian noise problem as a Maximum Likelihood (ML) estimation problem. The ML problem is very non linear and hard to solve. Some previous works focused on fi ...

2007