Gustaf Hendeby

Gustaf Hendeby

Associate Professor and Docent in Automatic Control

(Swe: Universitetslektor och docent i reglerteknik)

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Dr Gustaf Hendeby’s research is centered around statistical sensor fusion - primarily model based methods - with one foot in theory and one foot in more applied research. Sensor fusion provides methods that are used to combine information from many sources in order to produce a coherent view of phenomena observed in the world not obtainable from the separate sources on their own. With recent advances in sensor technology, resulting in smaller and more affordable sensors, a multitude of sensors are available almost everywhere; and much in life that we have learned to take for granted would not be possible without advanced sensor fusion. This knowledge drives his interest in the field.

Below he present his research divided into three main categories: core algorithm understanding, on-body sensor networks, and security and surveillance applications.

Core Algorithm Understanding

From the beginning, Dr Hendeby have been interested in the inner working of the algorithms that make up sensor fusion. By studying the Cramér-Rao lower bound (CRLB), he derived a method using intrinsic accuracy to indicate the potential performance gain from using nonlinear estimation and detection methods. Intrinsic accuracy is a noise property, which is closely related to Fisher information, that can be interpreted as measure of how informative a noise distribution is. In practice it acts like an effective variance term. To analyze the second order statistical properties of algorithms this way is very common, but does also in many situations not capture all aspects of an estimate that are important for the end result. By utilizing the above mentioned results, it is possible to provide insights into how to approach problems, and to beforehand answer the important question: “Is it reasonable to expect the results I need to solve this specific task?” This without having to resort to extensive Monte Carlo simulations.

In the same way, understanding how popular methods relate to each other makes it possible to make better design decisions early in the design process. Therefore, Dr Hendeby has analyzed the popular unscented Kalman filter (UKF) and managed to find clear connections to the classic extended Kalman filter (EKF), which in part explains its behavior. He currently pursue similar questions with Lic Eng Michael Roth. He has also analyzed the Rao-Blackewellized particle filter (RBPF), and was able to make connections between it and the important class of methods that are based on filter banks. Interpreting the RBPF this slightly different way can for instance be utilized to make efficient generalizable implementations.

Dr Hendeby’s interest for the particle filters (PFs) also resulted in the first complete PF implementation on a graphics card (GPU), which is a first step to make the PF realizable on low-cost readily available parallel hardware. A current fucus is to investigate the ensemble Kalman filter (EnKF), another approximate stochastic method that is heavily used in the geo-sciences, but so far not by the signal processing society. It could have the potential to solve extremely high-dimensional problems that would otherwise not be solvable at all.

On-Body Sensor Networks

At DFKI Dr Hendeby’s work was centered around on-body sensor networks consisting of inertial measurement units (IMUs) and in some cases other sensors such as cameras. Here he had the opportunity to work with and contribute to the whole signal processing chain from low-level sensor interaction to high-level fusion, and successfully derived methods to estimate the pose of the wearer of the sensors. In doing so we faced many practical problems, such as how to handle sensor synchronization, calibration of a heterogeneous sensor network, etc.

The sensor network was used in two European projects for: monitoring of physical exercise to ensure proper and safe execution (EU AAL project PAMAP (Physical Activity Monitoring for Aging People); and as input to workflow recognition and monitoring to aid in assembly tasks (EU FP7 COGNITO (Cognitive Workflow Capturing and Rendering with On-Body Sensor Networks). Dr Hendeby was technical coordinator of COGNITO comprising 7 partners from 4 countries with specialties such as biomechanics, computer vision, workflow recovery, computer graphics, human-machine interaction, and hardware manufacturing.

Stripped down wearable sensor network were also used in the PAMAP project to derive the wearer’s everyday daily activities. This is a problem that can be solved with good results using classifiers based on ensemble learners (Boosting).

Security and Surveillance Applications

At the Swedish Defence Research Agency (FOI), Dr Hendeby work with applied research (algorithm development, analysis, and implementation) in the area of security and surveillance. The focus is target tracking, where he overseea the tracking efforts in the group. He coordinates the design and development of an in-house tracking software. In this position he has spent considerable time with questions regarding decentralized and distributed fusion, and tracking systems in production code.

Recently, the focus of his research has been multi-sensor multi-target tracking to provide situational awareness, which is important aspects of both internal as well as external projects, e.g., the EU FP7 projects ADABTS (Automatic detection of abnormal behavior and threats in crowded spaces) and P5 (Privacy preserving perimeter protection project), he has been involved in. For this purpose he has developed a multi-hypothesis tracker (MHT), which is now used as an off-the-shelf toolbox to solve tracking tasks at FOI. In many cases detections in images are used as input, but the developed framework is not limited to this. Another application is tracking in sonar data, for which a Gaussian-Mixture Probability Hypothesis Distribution (PHD) filter solution was developed.

A topic of recent work, jointly with FOI and Linköping University, is passive tracking in acoustic networks and how the Doppler shifts in received signals can be used to track targets and also direction of arrival estimation. Another area of cooperation is SLAM.

Dr Hendeby is also in the Wild Life Security initiative and its Vinnova financed project “Smarta Savanner” intended to develop solutions to help protect and document endangered spices. In this project he coordinates and has technical lead for many of the different efforts to develop cheap, portable, and efficient methods and technical solutions to protect endangered wildlife. This allows him to work with both rather low-level sensor data as well as with higher level of abstraction in algorithm and demonstrator development.

Projects as PI

Supervised PhD Students

Current PhD Students

Past PhD Students


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Thesis work

PhD Thesis

Performance and Implementation Aspects of Nonlinear Filtering

Gustaf Hendeby, LiU-Tryck, Linköping, Sweden, February 2008.

Licentiate’s Thesis

Fundamental Estimation and Detection Limits in Linear Non-Gaussian Systems

Gustaf Hendeby, LiU-Tryck, Linköping, Sweden, November 2005. (Slides)