Gustaf Hendeby
Associate Professor and Docent in Automatic Control
(Swe: Universitetslektor och docent i reglerteknik)
- On-Line Appearance:
-
©2025. All rights reserved.
©2025. All rights reserved.
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.
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.
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).
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.
Audiovisual Tracking for Situational Awareness
Research will focus on methods for multi-target tracking based on audiovisual (AV) data. While sound can support and complement visual object tracking, the combination remains highly underutilized. The probabilistic framework of random finite sets (RFS) offers an appealing approach for inference when both data modes involve a random number of measurements per frame. RFS also provides a flexible foundation with strong data compression, which is crucial for real-time and collaborative settings, enabling decision support while meeting communication requirements. The research will involve developing novel RFS estimators for AV data, leveraging state-of-the-art detectors and classifiers from each domain. Achieving efficient and accurate tracking necessitates investigating fundamentals such as Cramér-Rao lower bounds, optimal sub-pattern assignment, and covariance intersection. These findings will be integrated into estimators like the Poisson multi-Bernoulli mixture for both single and collaborative multi-target tracking. The results will be disseminated and made available to a wider audience, including an open benchmark dataset with associated software
Funding from WASP for one PhD student.
Distributed Learning of Augmented State-Space Models
Methods for distributed learning of augmented state-space models will be researched. This class of models enables domain knowledge to be incorporated in the learning process to guarantee a minimum of perfor- mance and enable an efficient learning process; still they are flexible enough to permit learning of partially unknown model dynamics and inputs. Thus, the targeted methods are foreseen to play an important role to realize large-scale sensing systems. Focus of the research will be on learning of state-space models augmented with sparse Gaussian process models. To enable distributed and efficient learning of the mod- els, techniques for distributed Gaussian process regression, such as Bayesian commit machines and inner product quantization, will be integrated with distributed filter algorithms, such as covariance intersection, consensus, and diffusion Kalman filters. The use of information-gain based active learning strategies to streamline the learning process, both on local and global scale, will be investigated.
Funding from WASP for one PhD student, Sebastian Karsson.
The Map to Everywhere: Building HD Maps for Autonomous Driving Using Crowdsourcing
In collaboration with Zenseact.
Crowdsourced mapping, facilitated by low-cost sensors installed on vehicles that frequently travel on the roads, has emerged as an economically efficient and highly scalable solution for creating high-definition (HD) maps, which enable self-driving vehicles to perform precise localization and make informed decisions. This project aims to develop efficient and accurate crowdsourced mapping algorithms for autonomous driving through the collaboration of academia and industry.
This project contains three main work packages. First, we will develop mathematical frameworks for multi-robot simultaneous localization and mapping (SLAM) based on finite set statistics. Second, we will tailor the developed multi-robot SLAM algorithms to crowdsourced mapping. Lastly, we will verify the effectiveness of the developed crowdsourced mapping algorithms using both synthetic and real-world data. Throughout the project, the crowdsourced data collected by Zenseact’s production vehicles will guide the algorithm development. In addition, by leveraging the real-world data, we will also explore how to best combine model-based and data-driven methods.
This project will contribute to the academic research on crowdsourced mapping (and multi-robot SLAM in general) by providing new theories and algorithms, advancing the state-of-the-art. This project will also benefit Zenseact (and the Swedish automotive industry in general) by offering an economically efficient solution for creating its own up-to-date HD maps, giving it a competitive edge in the market.
Funding from WASP for one PostDoc (two years), Yuxuan Xia, Zenseact.
Multi-Sensor Image-Based Navigation
Aerial navigation in GNSS contested environments is a challenge for both manned and unmanned aircraft. This research project aims to study and develop technologies and algorithms to address the problem of aerial navigation without relying on GNSS, focusing in image-based navigation with multi-sensor fusion in order to get a precise localization of the aircraft. The proposed application to be used in this research project is related to an aerial transport of goods in a remote area (challenging environment and low risk area), using real time image processing and another embedded sensor processing to improve the aircraft localization, without using GNSS data. This application has a dual use (both military and civil).
Funding from Vinnova for one PostDoc, Jeong Min Kang.
Classical methods for filtering are married with machine learning methodology to provide better tracking and navigation solutions. Focus is on addressing the time update bottle neck in filtering, due to poor signal- to-noise (SNR), which has appeared as a result of the high requirements of situational awareness posed by autonomous systems. The goal is to be able to provide high quality filtering solutions, for tracking and navigation, as a result of being able to learn more accurate motion models.
Funding from WASP for one PhD student, Anton Kullberg.
Applied Research Platform for Sensor Fusion (CENIIT Project 17.2)
The aim of this project is to create a research group that using probabilistic learning methods addresses the needs introduced by the surge of less expensive sensors, and in the process to complement the existing group with competence in computer science and more knowledge of hardware. The goal of the proposal is to perform agile development of theory for emerging applications with early demonstrators and active involvement of students.
Funding from ZENITH.
Daniel Bossér. (Co-supervisor)
Peng Liu. (Co-supervisor)
Gustav Zetterqvist (Co-supervisor)
Chuan Huang (Co-supervisor)
Jakob Åslund (Co-supervisor)
Ashwani Kaul (Co-supervisor)
For more information:
For more information:
Performance and Implementation Aspects of Nonlinear Filtering
Fundamental Estimation and Detection Limits in Linear Non-Gaussian Systems