Particle filters for positioning, navigation, and tracking

@article{citeulike:1691407, 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 nonlinear 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 nonlinear 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 with 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}, author = {Gustafsson, F. and Gunnarsson, F. and Bergman, N. and Forssell, U. and Jansson, J. and Karlsson, R. and Nordlund, P. J. }, booktitle = {Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on]}, citeulike-article-id = {1691407}, doi = {10.1109/78.978396}, journal = {Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on]}, keywords = {filters, navigation, particle, positioning}, number = {2}, pages = {425--437}, posted-at = {2007-10-23 14:56:38}, priority = {2}, title = {Particle filters for positioning, navigation, and tracking}, url = {http://dx.doi.org/10.1109/78.978396}, volume = {50}, year = {2002} }

TY - JOUR ID - citeulike:1691407 L3 - citeulike-article-id:1691407 TI - Particle filters for positioning, navigation, and tracking T2 - Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on] JF - Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on] VL - 50 IS - 2 SP - 425 EP - 437 N2 - 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 nonlinear 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 nonlinear 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 with 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 KW - filters KW - navigation KW - particle KW - positioning AU - Gustafsson, F AU - Gunnarsson, F AU - Bergman, N AU - Forssell, U AU - Jansson, J AU - Karlsson, R AU - Nordlund, PJ PY - 2002/// UR - http://dx.doi.org/10.1109/78.978396 ER -