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Sensor Fusion

The goal in sensor fusion is to utilize information from spatially separated sensors of the same kind (so called sensor networks), sensors of different kind (so called heterogenous sensors) and finally on a more abstract level information sources in general in terms as for example geographical information systems (GIS).

Our activities involve the following areas

Filter theory

We perform research on filter theory for state estimation in dynamical systems, ranging from aspects in the classical (extended) Kalman filter, Gaussian mixture filters, the more recent unscented Kalman filter to the particle filter (PF) and PHD filter. The latter ones are current focus areas. Our contributions involve convergence analysis and marginalization approached to mitigate the curse of dimensionality. The PHD filter is studied for tracking multiple (possibly extended) targets.

Parameter estimation and calibration

The general problem falls under the heading system identification. From a sensor fusion perspective, we study Simultaneous Localization And Mapping (SLAM) and sensor calibration. SLAM aims at solving two tasks in the same filter, namely localization of the host vehicle and at the same time building a map (GIS) of the surrounding. Both EKF and PF based approaches have been studied.

Situation awareness

Situation awareness involves tasks as sensor management, decision and information theory, and collision avoidance (CA). CA is one important application of state estimation, where the estimated state is used to assess the risk for a conflict, and also for evaluating different evasive maneuvers, and we focus on automotive and aircraft applications.

Applications

We are keen to always work on real data in our research, and this applies to all projects. More application oriented research involve localization based on fusion of sensor observations and GIS. Concrete applications are terrain navigation of aircraft using altitude GIS, terrain navigation of underwater vehicles using bottom depth GIS, localization in road networks using road GIS, surface ship navigation using radar and sea chart GIS, migrating birds based on light measurements, etc.

Examples

Magnetic content

A metal frame table and its estimated magnetic content.



Real-time positioning

Real-time positioning of firefighters during a search increase the safety and efficiency of the operation.


Informationsansvarig: Fredrik Gustafsson
Senast uppdaterad: 2017-08-29