Home News Participants Projects People MCI-Workshops Conferences Publications Links



There are 13 projects for early stage researchers (ESR, 3 year projects) and 3 projects
for experienced researchers (ER, 2 year projects).

ESR projects


Group tracking & distributed estimation in Wireless Networks and Systems

PETROV Nikolay

Host: Lancaster University.

Supervisors: MIHAYLOVA Lyudmila and MARKARIAN Garik

Description: Algorithmic aspects and theoretical underpinnings of distributed estimation, detection and decision making in wireless networks and systems will be studied, including methods for distributed estimation and inference, in particular Gaussian mixture models combined with particle filtering; and methods for group object tracking related with the structure of the sensor network.

Structured models for non-linear filtering

ROTH Michael

Host: Linköping University

Supervisors: GUSTAFSSON Fredrik, ÖZKAN Emre and SCHÖN Thomas

Description: A particular promising area for the Marginalized Particle Filter (MPF) is in Simultaneous Localization And Mapping (SLAM). State of the art is the fast SLAM algorithm which in itself is a MPF. However, the state vector used in literature is lowdimensional, including horizontal position and heading angle only. Linköping University has recently found that a second layer of marginalization could extend the scope to much more complex models.


Track-Before-Detect based on acoustic sensors


Host: Saab AB

Supervisor: SVIESTINS Egils and EKMAN Mats
Description: Problems related to acoustic sensors are varying characteristics of sound, its propagation and the background noise. The traditional way of resolving these problems is based on thresholding, resulting in a great loss of information. A much better performance is expected by using raw data in the tracking process. Saab believes Track-Before-Detect based on particle filters has great potential.


Integrated information extraction


Host: Thales Nederland B.V.

Supervisor: DRIESSEN Hans

Description: Optimal schemes for appropriately exploiting all the available information are currently not available. In principle particle filters allow for optimal solutions once the modelling has been performed. Therefore, correct modelling and an efficient particle filter implementation will be necessary in order to be able to exploit all the available information.


Localization in Unknown Urban Environment


Host: Lancaster University.

Supervisors: MIHAYLOVA Lyudmila and MARKARIAN Garik

Description: Solving localization problems in an urban environment meets different challenges due to obscuration, non-line-of sight, fading and other noises and uncertainties. Therefore, advanced localization techniques will be investigated using: angle of arrivals, time difference of arrivals, Non-Line-Of-Sight (NLOS) or Received signal strength (RSS) data. Bayesian inference techniques have proven their power in such cases and will be the main framework for localization.

Localization and calibration of ground sensors


Host: Saab AB

Supervisor: SVIESTINS Egils and EKMAN Mats
Description: Locating multiple ground sensors where satellite navigation is unreliable makes it difficult to ensure consistent information. The project aims at developing methods for locating, aligning and calibrating a large number of sensors so that consistent information is given, which is a prerequisite for reliable tracking.

Hybrid Sensory Networks
Host: Rinicom Ltd.
Supervisors: KOLEV George and SONANDER Sean
Description: The conventional approach to sensory networks assumes a homogeneous network infrastructure. However, most real-life scenarios are based upon the use of hybrid networks. This creates a number of issues related to the reliability of the received data, remote calibration of the sensors and provision of the same quality of service to wireless and wired sensor nodes. This sub project aims at developing and implementing a prototype hybrid network which could dynamically adapt its performance.

GIS for multiple sensor air-to-ground surveillance and traffic monitoring


Host: Fraunhofer FKIE

Supervisor: ULMKE Martin
Description: Context information made available by modern Geographical Information Systems (GIS) can greatly improve the results of sensor-driven ground surveillance. On the other hand, so-called “tracks” of road moving objects provide approximations to the underlying roads, which can be refined to high-precision road and up-to-date road maps. In other words, valuable context information can be gathered from sensor data fusion.


Sensor and map assistance for search and rescue teams
Host: Fraunhofer FKIE
Supervisor: ULMKE Martin
Description: Modern fire fighters or search and rescue teams are equipped with humanborne communication and sensor systems collecting and distributing information on physiological parameters of the persons in action and surveillance sensors for perceiving their surrounding. For enhancing their efficiency in time critical and dangerous situations, this information has to be fused with mapping information.


Localization and mapping using magnetometers

KOK Manon

Host: Linköping University
Supervisors: SCHÖN Thomas and GUSTAFSSON Fredrik 
Description: Magnetometers are generally used as a source of heading information, leading to erroneous estimates when used in the vicinity of magnetic distortions. The presence of magnetic distortions can, however, also be regarded as an additional source of information about both the sensor's position and orientation. The project is concerned with obtaining a map of the magnetic environment as well as with a particle filter implementation to estimate the sensor's 6D pose within this map, based on magnetometer data in combination with inertial data from a MEMS IMU. An ultimate goal is doing simultaneous localization and mapping (SLAM) with magnetometers.

Energy efficient sensor scheduling for sensor networks of simple sensors

AOKI Edson Hiroshi

Host: Twente University

Supervisors: MANDAL Pranab and BAGCHI Arun
Description: Particle filter based processing, combined with dynamic programming and statistical optimisation techniques will be studied aiming at more efficient and better scheduling algorithms. Important issues are to deal with multiple objects or events and parametric uncertainties in the network.


Sensor management aspects


Host: Thales Nederland B.V.

Supervisor: BOERS Yvo
Description: Sensors or sensor systems can often operate in many different modes or their settings may even vary over a continuum of possibilities. Depending on the task(s) different settings could be applied and maybe even changed on-line or in real-time. The objective is to optimize sensor tuning and incorporate this with the array of new processing techniques.


Location and Positioning on WiMAX Networks


Host: Rinicom Ltd.

Supervisors: KOLEV George and SONANDER Sean

Description: Most current positioning systems is either constrained to outdoor environments or limited to a particular building or campus with installed location infrastructure. The objective is to us WiMAX networks for positioning. Multiple-Input Multiple Output (MIMO) networks, Adaptive Modulation and Coding as well as Cross Layer Optimisation will be considered. 

ER projects

Multiple target tracking in a ground sensor network


Host: Saab AB

Supervisor: SVIESTINS Egils and EKMAN Mats
Description: If two sensors indicate the presence of a target, are there two targets or is it one target detected by two sensors? And if there are two targets, how does one prevent mixing them up as they move around? So far such problems have not been sufficiently

Exploiting prior sensor & environmental knowledge

PAPI Francesco

Host: Thales Nederland B.V.

Supervisor: PODT Martin
Description: Prior sensor knowledge is oftentimes ignored or used only on an ad hoc basis. This project seeks to use prior sensor knowledge and to exploit external information to increase system performance.

Indoor orientation and tracking system using MEMS


Host: Xsens Technologies B.V.

Supervisor: LUINGE Henk
Description: The objective is to develop an indoor position and orientation tracking system for general indoor environments. Due to the high dimensionality, a sub optimal nonlinear tracking solution must be defined. Accurate tracking while keeping track of only a limited number of particles and choosing the particles to reach highest pose observability need to be solved.