Novel approach to nonlinear/non-Gaussian Bayesian state estimation Export

@article{citeulike:1815463, abstract = {An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter}, author = {Gordon, N. J. and Salmond, D. J. and Smith, A. F. M.}, booktitle = {Radar and Signal Processing, IEE Proceedings F}, citeulike-article-id = {1815463}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=210672}, day = {06}, journal = {Radar and Signal Processing, IEE Proceedings F}, keywords = {bayesian, estimation}, month = {August}, number = {2}, pages = {107--113}, posted-at = {2009-03-19 00:28:53}, priority = {2}, title = {Novel approach to nonlinear/non-Gaussian Bayesian state estimation}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=210672}, volume = {140}, year = {2002} }

TY - JOUR ID - citeulike:1815463 L3 - citeulike-article-id:1815463 N2 - An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter IS - 2 JF - Radar and Signal Processing, IEE Proceedings F EP - 113 TI - Novel approach to nonlinear/non-Gaussian Bayesian state estimation VL - 140 T2 - Radar and Signal Processing, IEE Proceedings F SP - 107 KW - bayesian KW - estimation AU - Gordon, NJ AU - Salmond, DJ AU - Smith, AFM PY - 2002/08/06/ UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=210672 ER -