Gustaf Hendeby: Research
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 hia 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.
Supervised PhD Students
Current PhD Students
- Jonatan Olofsson. (Co-supervisor, NTNU)
- Lic Parinaz Kasebzadeh. Parameter Estimation for Mobile Positioning Applications, Oct 2017. (Co-supervisor)
- Lic Kamiar Radnosrati. On Timing-Based Localization in Cellular Radio Networks, June 2018. (Co-supervisor)
- Lic. Martin Lindfors. Frequency Tracking for Speed Estimation, Aug 2018. (Co-supervisor)
- Lic. Du Ho. Some results on closed-loop identification of quadcopters, Nov 2018. (Co-supervisor)
- Per Boström-Rost (SAAB Aeronotics).
- Andreas Bergström (Ericsson). (Co-supervisor)
- Kristin Nielsen (Epiroc).
Past PhD Students
- Dr. Clas Veibäck. Tracking the Wanders of Nature, Dec 2018.
- Dr Michael Roth. Advanced Kalman Filtering Approaches to Bayesian State Estimation, April 2017. (Co-supervisor)
- Lic. Hanna Nyqvist. On Pose Estimation in Room-Scaled Environments, Dec 2016.
- Lic George Mathai. Direction of Arrival Estimation of Wideband Acoustic Wavefields in a Passive Sensing Environment, Sept, 2015. (Co-supervisor)
- Dr Attila Reiss. Personalized Mobile Physical Activity Monitoring for Everyday Life, Jan 2014. (Co-supervisor)
- Lic Marek Syldatk. On Calibration of Ground Sensor Networks, Sept 2013. (Co-supervisor)
For more information:
Nonlinear filtering is an important standard tool for information and sensor fusion applications, e.g., localization, navigation, and tracking. It is an essential component in surveillance systems and of increasing importance for standard consumer products, such as cellular phones with localization, car navigation systems, and augmented reality. This thesis addresses several issues related to nonlinear filtering, including performance analysis of filtering and detection, algorithm analysis, and various implementation details.
The most commonly used measure of filtering performance is the root mean square error (RMSE), which is bounded from below by the Cramér-Rao lower bound (CRLB). This thesis presents a methodology to determine the effect different noise distributions have on the CRLB. This leads up to an analysis of the intrinsic accuracy (IA), the informativeness of a noise distribution. For linear systems the resulting expressions are direct and can be used to determine whether a problem is feasible or not, and to indicate the efficacy of nonlinear methods such as the particle filter (PF). A similar analysis is used for change detection performance analysis, which once again shows the importance of IA.
A problem with the RMSE evaluation is that it captures only one aspect of the resulting estimate and the distribution of the estimates can differ substantially. To solve this problem, the Kullback divergence has been evaluated demonstrating the shortcomings of pure RMSE evaluation.
Two estimation algorithms have been analyzed in more detail; the Rao-Blackwellized particle filter (RBPF), by some authors referred to as the marginalized particle filter (MPF), and the unscented Kalman filter (UKF). The RBPF analysis leads to a new way of presenting the algorithm, thereby making it easier to implement. In addition the presentation can possibly give new intuition for the RBPF as being a stochastic Kalman filter bank. In the analysis of the UKF the focus is on the unscented transform (UT). The results include several simulation studies and a comparison with the Gauss approximation of the first and second order in the limit case.
This thesis presents an implementation of a parallelized PF and outlines an object-oriented framework for filtering. The PF has been implemented on a graphics processing unit (GPU), i.e., a graphics card. The GPU is a inexpensive parallel computational resource available with most modern computers and is rarely used to its full potential. Being able to implement the PF in parallel makes new applications, where speed and good performance are important, possible. The object-oriented filtering framework provides the flexibility and performance needed for large scale Monte Carlo simulations using modern software design methodology. It can also be used to help to efficiently turn a prototype into a finished product.
Many methods used for estimation and detection consider only the mean and variance of the involved noise instead of the full noise descriptions. One reason for this is that the mathematics is often considerably simplified this way. However, the implications of the simplifications are seldom studied, and this thesis shows that if no approximations are made performance is gained. Furthermore, the gain is quantified in terms of the useful information in the noise distributions involved. The useful information is given by the intrinsic accuracy, and a method to compute the intrinsic accuracy for a given distribution, using Monte Carlo methods, is outlined.
A lower bound for the covariance of the estimation error for any unbiased estimator is given by the Cramér-Rao lower bound (CRLB). At the same time, the Kalman filter is the best linear unbiased estimator (BLUE) for linear systems. It is in this thesis shown that the CRLB and the BLUE performance are given by the same expression, which is parameterized in the intrinsic accuracy of the noise. How the performance depends on the noise is then used to indicate when nonlinear filters, e.g., a particle filter, should be used instead of a Kalman filter. The CRLB results are shown, in simulations, to be a useful indication of when to use more powerful estimation methods. The simulations also show that other techniques should be used as a complement to the CRLB analysis to get conclusive performance results.
For fault detection, the statistics of the asymptotic generalized likelihood ratio (GLR) test provides an upper bound on the obtainable detection performance. The performance is in this thesis shown to depend on the intrinsic accuracy of the involved noise. The asymptotic GLR performance can then be calculated for a test using the actual noise and for a test using the approximative Gaussian noise. Based on the difference in performance, it is possible to draw conclusions about the quality of the Gaussian approximation. Simulations show that when the difference in performance is large, an exact noise representation improves the detection. Simulations also show that it is difficult to predict the exact influence on the detection performance caused by substituting the system noise with Gaussian noise approximations.
Associate Professor and Docent in Automatic Control
(Swedish: Universitetslektor och docent i reglerteknik)
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Page responsible: Gustaf Hendeby
Last updated: 2018-12-31