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Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2008 (English)In: Automatica, ISSN 0005-1098Article in journal (Refereed) Submitted
Abstract [en]

Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

Place, publisher, year, edition, pages
Keyword [en]
Fault detection, Parity space sensor fusion, Inertial sensors, Magnetometer
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-15501OAI: diva2:117419
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2013-09-15Bibliographically approved
In thesis
1. Estimation and Detection with Applications to Navigation
Open this publication in new window or tab >>Estimation and Detection with Applications to Navigation
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection.

The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution.

Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably.

Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. 174 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1216
Navigation, SLAM, Particle Filter, Estimation
National Category
Engineering and Technology
urn:nbn:se:liu:diva-14956 (URN)978-91-7393-785-6 (ISBN)
Public defence
2008-11-05, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
The third article in this thesis is included with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this material, you agree to all provisions of the copyright laws protecting it. Please be advised that wherever a copyright notice from another organization is displayed beneath a figure, a photo, a videotape or a Powerpoint presentation, you must get permission from that organization, as IEEE would not be the copyright holder.Available from: 2008-11-12 Created: 2008-10-03 Last updated: 2009-05-18Bibliographically approved

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