From WWWwikiEn<span style="display: none;">[[Image:Fusion08_Wang.pdf]][[Image:HCI07_Wang.pdf]][[Image:WPNC07_Wang.pdf]][[Image:MFI06_Wang.pdf]]</span> <span style="display: none;">[[Image:Fusion08_Wang.pdf]][[Image:HCI07_Wang.pdf]][[Image:WPNC07_Wang.pdf]][[Image:MFI06_Wang.pdf]]</span>

Hui Wang

	Dipl.-Ing. External Ph.D. Student
	Address:	Siemens AG Corporate Technology Information and Communications 4 81730 München
	Walk-in hours:	on arrangement
	Phone:	+49-89-636-43765
	E-mail:	hui.wang.ext@siemens.com

Publications

Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 290-295, Seoul, Republic of Korea, August, 2008.

URL

Author : Chongning Na, Hui Wang, Dragan Obradovic, Uwe D. Hanebeck
Title : Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Date : August 2008

Abstract

Many distributed inference problems in wireless
sensor networks can be represented by probabilistic graphical
models, where belief propagation, an iterative message passing
algorithm provides a promising solution. In order to make the
algorithm efficient and accurate, messages which carry the
belief information from one node to the others should be
formulated in an appropriate format. This paper presents two
belief propagation algorithms where non-linear and
non-Gaussian beliefs are approximated by Fourier density
approximations, which significantly reduces power
consumptions in the belief computation and transmission. We
use self-localization in wireless sensor networks as an example to
illustrate the performance of this method.

Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 445-450, Seoul, Republic of Korea, August, 2008.

URL

Author : Hui Wang, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Date : August 2008

Abstract

Typical WLAN based indoor positioning systems
take the received signal strength (RSS) as the major information
source. Due to the complicated indoor environment, the RSS
measurements are hard to model and too noisy to achieve a
satisfactory 3-D accuracy in multi-floor scenarios. To enhance
the performance of WLAN positioning systems, extra information
sources could be integrated. In this paper, a Bayesian
framework is applied to fuse multi-information sources and
estimate the spatial and time varying parameters simultaneously
and adaptively. An application of this framework, which
fuses pressure measurements, a topological building map with
RSS measurements, and simultaneously estimates the pressure
sensor bias, is investigated. Our experiments indicate that the
localization performance is more accurate and robust by using
our approach.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-7, Cologne, Germany, July, 2008.

Author : Hui Wang, Andrei Szabo, Joachim Bamberger, Dietrich Brunn, Uwe D. Hanebeck
Title : Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Date : July 2008

Abstract

Indoor WLAN positioning should be modeled as a nonlinear
and non-Gaussian dynamic system due to the complex indoor environment,
radio propagation and motion behaviour. The aim of this paper is to
analyze different filtering strategies for real life indoor WLAN
positioning systems. The performance criteria for the comparison are
the mean of localization errors and computational complexity.
Three nonlinear filters are analyzed: Fourier density approximation (FF),
particle filter (PF) and grid-based filter (GF), which are representatives for
deterministic and random density approximation approaches.
Our experimental results help to choose the appropriate filtering
techniques under different resource limitations.

International Conference on Human-Computer Interaction (HCI 2007), Peking, China, July, 2007.

Author : Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : Enhancing the Map Usage for Indoor Location-Aware Systems
In : International Conference on Human-Computer Interaction (HCI 2007)
Date : July 2007

Abstract

Location-aware systems are receiving more and more interest in both
academia and industry due to their promising prospective in a broad
category of so-called Location-Based-Services (LBS). The map interface
plays a crucial role in the location-aware systems, especially for
indoor scenarios. This paper addresses the usage of map information
in a Wireless LAN (WLAN)-based indoor navigation system. We describe
the benefit of using maNMp information in multiple algorithms of
the system, including radio-map generation, tracking, semantic positioning
and navigation. Then we discuss how to represent or model the indoor
map to fulfill the requirements of intelligent algorithms. We believe
that a vector-based multi-layer representation is the best choice
for indoor location-aware system.

Workshop on Positioning, Navigation and Communication, (WPNC 2007), Hanover, Germany, March, 2007.

Author : Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors
In : Workshop on Positioning, Navigation and Communication, (WPNC 2007)
Date : March 2007

Abstract

Indoor positioning systems based on Wireless LAN
(WLAN) are being widely investigated in academia and industry.
Meanwhile, the emerging low-cost MEMS sensors can also be used
as another independent positioning source. In this paper, we
propose a pedestrian tracking framework based on particle filters,
which extends the typical WLAN-based indoor positioning systems
by integrating low-cost MEMS accelerometer and map
information. Our simulation and real world experiments indicate a
remarkable performance improvement by using this fusion
framework.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 426-432, Heidelberg, Germany, September, 2006.

Author : Hui Wang, Henning Lenz, Andrei Szabo, Uwe D. Hanebeck, Joachim Bamberger
Title : Fusion of Barometric Sensors, WLAN Signals and Building Information for 3-D Indoor/Campus Localization
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Date : September 2006

Abstract

Location estimation in indoor/campus
environments has attracted much interest for its broad
applications. Many applications (e.g. personnel security)
require not only the 2-D coordinate but also the floor index
where the mobile users are situated. However, most of the
current location systems cannot provide the floor information
accurately and robustly. In this paper, we propose a 3-D
localization scheme which fuses the barometric sensor with
Wireless LAN (WLAN) signals and building information. Our
experiments show that this fusion scheme can both identify the
floor index without errors and improve the horizontal
localization accuracy. Moreover, since the barometric sensor is
quite simple and cheap, it would bring almost no increase in
system costs.