| 1. |
- Gustafsson, Fredrik, et al.
(författare)
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Particle Filters for Positioning, Navigation and Tracking
- 2001
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter based algorithms. Here the use of non-linear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map, and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable to satellite navigation (as GPS), but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.
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| 2. |
- Gustafsson, Fredrik, et al.
(författare)
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Sensor Fusion for Accurate Computation of Yaw Rate and Absolute Velocity
- 2001
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Ingår i: Proceedings of the SAE 2001 World Congress. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International.
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Konferensbidrag (refereegranskat)abstract
- In the presented sensor fusion approach, centralized filtering of related sensor signals is used to improve and correct low price sensor measurements. From this, we compute high-quality state information as drift-free yaw rate and exact velocity (accounting for unknown tire radius and slipping wheels on 4WD vehicles). The basic tool here is a Kalman filter supported by change detection for sensor diagnosis. Results and experience of real-time implementations are presented.
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| 3. |
- Gustafsson, Fredrik, et al.
(författare)
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Virtual Sensors of Tire Pressure and Road Friction
- 2001
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Ingår i: Proceedings of the SAE 2001 World Congress. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International.
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Konferensbidrag (refereegranskat)abstract
- The idea of a virtual sensor is to extract information of parameters that cannot be measured directly, or at least would require very costly sensors, by only using available information. Virtual sensors are described for the friction between road and tire, the tire inflation pressure and wheel imbalance. There are certain interconnections between these virtual sensors so they are preferably implemented in one unit. Results from a real-time implementation, using mainly sensor information from the CAN bus, are given.
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| 4. |
- Nordlund, Per-Johan, et al.
(författare)
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A Framework for Particle Filtering for Positioning, Navigation and Tracking
- 2001
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Ingår i: Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing. - : IEEE. - 0780370112 ; , s. 34-37
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Konferensbidrag (refereegranskat)abstract
- A framework for positioning, navigation and tracking problems using particle filters (recursive Monte Carlo methods) is developed. Automotive and airborne applications, approached in this framework, have proven a numerical advantage over classical Kalman filter based algorithms. Here the use of non-linear measurement models and non-Gaussian measurement noise is the main explanation for the improvement in accuracy, and models for relevant sensors are surveyed.
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