The Identity Management Kalman Filter (IMKF)
Abstract
Tracking posteriors estimates for problems with data association uncertainty is one of the big open problems in the literature on filtering and tracking. This paper presents a new filter for online tracking of many individual objects with data association ambiguities. It tightly integrates the continuous aspects of the problem -- locating the objects -- with the discrete aspects -- the data association ambiguity. The key innovation is a probabilistic information matrix that efficiently does identity management, that is, it links entities with internal tracks of the filter, enabling it to maintain a full posterior over the system amid data association uncertainties. The filter scales quadratically in complexity, just like a conventional Kalman filter. We derive the algorithm formally and present large-scale results. Download: Bibtex: @INPROCEEDINGS{ Schumitsch-RSS-06, AUTHOR = {B. Schumitsch and S. Thrun and L. Guibas and K. Olukotun}, TITLE = {The Identity Management {Kalman} Filter (IMKF)}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2006}, ADDRESS = {Philadelphia, USA}, MONTH = {August}, DOI = {10.15607/RSS.2006.II.029} }