The Unscented Particle Filter

@inproceedings{Merwe2001, abstract = {In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental...}, author = {van der Merwe, Rudolph and de Freitas, Nando and Doucet, Arnaud and Wan, Eric }, booktitle = {Advances in Neural Information Processing Systems 13}, citeulike-article-id = {347164}, keywords = {filter, particle}, month = {Nov}, posted-at = {2007-08-07 14:47:43}, priority = {3}, title = {The Unscented Particle Filter}, url = {http://citeseer.ist.psu.edu/385934.html}, year = {2001} }

TY - CONF ID - Merwe2001 L3 - citeulike-article-id:347164 TI - The Unscented Particle Filter T2 - Advances in Neural Information Processing Systems 13 N2 - In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental... KW - filter KW - particle AU - van der Merwe, Rudolph AU - de Freitas, Nando AU - Doucet, Arnaud AU - Wan, Eric PY - 2001/Nov// UR - http://citeseer.ist.psu.edu/385934.html ER -