Signal processing

Research group leader: ​Professor Tomas McKelvey

Our researchers in Signal processing are listed below.

About the Signal processing research area

Today, you find Digital Signal Processing (DSP) in your mobile phone, CD player, automobile, washing machine etc. In short, DSP is everywhere. Basically, DSP converts raw sensor input into useful information. An important goal is to replace expensive sensors with cheap ones, combined with smart signal processing. Another goal is to measure what was previously not possible to measure.


We have a strong background in model-based signal processing using, among other things, tools from mathematical statistics and numerical methods. Our “classical” applications are found in wireless communications and radar signal processing, where we work with channel estimation, adaptive antennas and high-resolution methods.
However, it is our ambition to spread the technology to other areas, where modern signal processing can advance the state of the art, for example through refined interpretation of measurements. Examples of such projects include:
  • analysis of power quality signals
  • landmine detection
  • automotive signal processing (engine control and active safety applications)
  • video communication
  • analysis of EEG and other biomedical signals

The mixture of basic research in statistical signal processing and new application areas has been fruitful and we plan to continue in the same spirit.

Research projects

> Closed-loop control of combustion engines
A vital part of a system for closed-loop combustion control is sensors providing combustion information that can be fed back to the controller. This project investigates the use of a crankshaft integrated torque sensor for this purpose. The work includes development of methods for both combustion property estimation and closed-loop combustion property control...

> Light-Duty Diesel Engine for 2012 - Engine Control
This project investigates the use of information from crankshaft torque and ion current measurements for closed-loop diesel engine control. A large portion of the work therefore consists of developing methods for extracting combustion information from the measured signals...

> Filtering techniques for sensor fusion
The project aims at improving automotive safety and has two phases. First, improved models and methods for estimating statistical moments for use in tracking filters are explored, for sensors typically used in automotive safety systems, e.g., radar...

> Multi-Antenna Technologies for Wireless Access and Backhaul (MATWAB)
In this project, we investigate the potential of two emerging technologies in the area of wireless communications. More specifically, we explore the concept of large-scale (or massive) MIMO systems which are anticipated to deploy base-stations with hundreds of low-power antennas...

> Ground target tracking with airborne radar
This project concerns analysis and development of new algorithms for target tracking in complicated environments, involving multiple targets and uncertain measurements due to partial occlusion, heavy clutter, etc.

> Microwave tomography for pharmaceutical processes
This project aims at developing a sensor array of antennas capable of microwave tomography for pharmaceutical processes, together with a post-processing algorithm for data analysis...

> Model-based reconstruction and classification based on near-field microwave measurements

> Non hit car and trucks
The project is focusing on jointly developing technologies to reduce accident risks for both passenger cars and commercial vehicles and particularly addressing the situations at which today’s active safety systems are not yet sufficient. To reach the goals brand new and improved safety functions with real-life benefits need to be invented across the whole safety domain, ranging from strategic drive to in-crash activities...

> Object tracking for video surveillance and traffic safety
Research in this project mainly includes: video object tracking captured by one moving camera, from multiple camera, captured by visual and IR cameras. The primary methods in our study include, for example: particle filters, mean shift, local feature points, differentiable manifolds (e.g. Grassmann, Riemannian), online domain-shift learning, multiview geometry, and sensor fusion. In terms of problem solving, we focus on tracking dynamic objects through complex scenes containing long-term partial/full occlusions, large objects with significant out-of-plane pose changes. We track generic objects, e.g. humans, faces, vehicles, animals, workshop tools, and many more…
> Automatic classification and diagnostics of power system disturbance recordings
The aim is to automatic classification of the underlining causes of power system disturbances from a large amount of measurement data. This includes analyzing and characterizing power system disturbances, searching for common phenomena behind each type of underlying causes, and give recommendations...


> Human activity and behavior analysis and classification with applications
Research in this project mainly includes: analysis of a range of activities and behaviors (e.g. in elderly care centers, out-patient care centers, office environments, and vehicle drivers). This includes the recognition of different (or, specific) activities, analysis of individual activities to obtain a range of long-term (or short-term) statistics on normal activity/behaviors for purposes such as improving elderly care, detecting abnormality, office environment improvement, and safety driving. Main methods we investigate in this project are: novel machine learning and pattern classification methods, effective feature descriptors for activities/behaviors, statistical modeling and parameter estimations for normal/abnormal activities, and generate recommendations or trigger actions...
> DTI methods for MRI brain image analysis
Lesions affecting the visual pathways in the human brain are common and may cause reduced visual acuity or visual field defects, either directly or as a result of surgery. These pathways can be visualised using tractography. The procedure is based on a combination of a magnetic resonance imaging technique known as diffusion tensor imaging (DTI) and computer-based image analysis...

> Parameter estimation based on sparse modeling
In this project, we focus on two particular problems related to model uncertainty. One concerns calibration using interpolation and smoothing, and a particular application is antenna array signal processing. The other is tracking of moving targets using multiple motion models...

> Optimization of sensors and sensor arrays
In this project the aim is to investigate how the robustness and accuracy of a sensor system is influenced by the number of sensors, their frequency range of operation and their specific locations around the measurement region...


> Waveform diversity in waveband radar

> Cohabitation of radar and communication systems

> Robust engine systems

> Visual quality measures



Published: Wed 05 Sep 2012. Modified: Wed 15 Feb 2017