| 1. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
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A Graphics Processing Unit Implementation of the Particle Filter
- 2007
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Ingår i: Proceedings of the 15th European Statistical Signal Processing Conference. - Linköping : European Association for Signal, Speech, and Image Processing. - 9788392134022 ; , s. 1639-1643
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Konferensbidrag (refereegranskat)abstract
- Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.
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| 2. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
-
A New Formulation of the Rao-Blackwellized Particle Filter
- 2007
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Ingår i: Proceedings of the 14th IEEE/SP Statistical Signal Processing Workshop. - Linköping : Linköping University Electronic Press. - 9781424411986 ; , s. 84-88
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Konferensbidrag (refereegranskat)abstract
- For performance gain and efficiency it is important to utilize model structure in particle filtering. Applying Bayes- rule, present linear Gaussian substructure can be efficiently handled by a bank of Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF), by some authors denoted the marginalized particle filter (MPF), and usually presented in a way that makes it hard to implement in an object oriented fashion. This paper discusses how the solution can be rewritten in order to increase the understanding as well as simplify the implementation and reuse of standard filtering components, such as Kalman filter banks and particle filters. Calculations show that the new algorithm is equivalent to the classical formulation, and the new algorithm is exemplified in a target tracking simulation study.
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| 3. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
-
Graphics Processing Unit Implementation of the Particle Filter
- 2006
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.
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| 4. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
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Performance Issues in Non-Gaussian Filtering Problems
- 2006
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Ingår i: Proceedings of the 2006 IEEE Nonlinear Statistical Signal Workshop. - Linköping : Linköping University Electronic Press. - 9781424405817 ; , s. 65-68
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Konferensbidrag (refereegranskat)abstract
- Performance for many filtering problems is usually measured using the second order moment. For non-Gaussian application this measure is not always sufficient. In the paper the Kullback divergence is extensively used to compare distributions. Several estimation techniques are compared, and methods such as the particle filter are shown to give superior performance over some classical second-order estimators.
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| 5. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
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Recursive Triangulation Using Bearings-Only Sensors
- 2006
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Ingår i: Proceedings of the 2006 IEE Seminar on Target Tracking: Algorithms and Applications. - Linköping : Institution of Electrical Engineers (IEE). - 086341608X - 9780863416088 ; , s. 3-10
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Konferensbidrag (refereegranskat)abstract
- Recursive triangulation, using a bearings-only sensor, is investigated for a fly-by scenario. In a simulation study, several estimators are compared, fundamental estimation limits are calculated for different measurement noise assumptions. The quality of the estimated state distributions is evaluated.
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| 6. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
-
Target Tracking Performance Evaluation - A General Software Environment for Filtering
- 2007
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Ingår i: Proceedings of the 2007 IEEE Aerospace Conference. - Linköping : Linköping University Electronic Press. - 1424405254 - 1424405254 ; , s. 1-13
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Konferensbidrag (refereegranskat)abstract
- In this paper, several recursive Bayesian filtering methods for target tracking are discussed. Performance for target tracking problems is usually measured using the second-order moment. For nonlinear or non-Gaussian applications, this measure is not always sufficient. The Kullback divergence is proposed as an alternative to mean square error analysis, and it is extensively used to compare estimated posterior distributions for various applications. The important issue of efficient software development, for nonlinear and non-Gaussian estimation, is also addressed. A new framework in C++ is detailed. Utilizing modern design techniques an object oriented filtering and simulation framework is provided to allow for easy and efficient comparisons of different estimators. The software environment is extensively used in several applications and examples.
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| 7. |
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| 8. |
- Karlsson, Rickard, 1970-, et al.
(författare)
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Bayesian Position Estimation of an Industrial Robot using Multiple Sensors
- 2004
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Ingår i: Proceedings of the 2004 IEEE Conference on Control Applications. - Linköping : Linköping University Electronic Press.
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Konferensbidrag (refereegranskat)abstract
- A modern industrial robot control system is often only based upon measurements from the motors of the manipulator. To perform good tra-ectory tracking on the arm side of the robot a very accurate description of the system must therefore be used. In the paper a sensor fusion technique is presented to achieve good estimates of the position of the robotusing a very simple model. By using information from an accelerometer at the tool of the robot the effect of unmodelled dynamics can be measured. The estimate of the tool position can be improved to enhance accuracy. We formulate the computation of the position as a Bayesian estimation problem and propose two solutions. The first solution uses the extended Kalman fillter (EKF) as a fast but linearized estimator. The second uses the particle fillter which can solve the Bayesian estimation problem without linearizations or any Gaussian noise assumptions. Since the aim is to use the positions estimates to improve position with an iterative learning control method, no computational constraints arise. The methods are applied to experimental data from an ABB IRB1400 commercial industrialrobot and to data from a simulation of a realistic flexible robot model, showing a significant improvement in position accuracy.
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| 9. |
- Karlsson, Rickard, 1970-, et al.
(författare)
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Bayesian Surface and Underwater Navigation
- 2004
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.
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| 10. |
- Karlsson, Rickard, 1970-, et al.
(författare)
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Complexity Analysis of the Marginalized Particle Filter
- 2004
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Ingår i: Proceedings of the 5th Conference on Computer Science and Systems Engineering. - Linköping : Linköping University Electronic Press. ; , s. 169-
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper the computational complexity of the marginalized particle lter is analyzed. We introduce an equivalent flop measure to capture floating-point operations as well as other features, which cannot be measured using flops, such as the complexity in generating random numbers and performing the resampling. From the analysis we conclude how to partition the estimation problem in an optimal way for some common target tracking models. Some guidelines on how to increase performance based on the analysis is also given. In an extensive Monte Carlo simulation we study different computational aspects and compare with theoretical results.
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