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The Gaussian mixture probability hypothesis density filter

edgesoul 添加于 2010-3-7 08:26 | 1552 次阅读 | 0 个评论

作者
Vo B-ngu, Ma W-kin
摘要
Abstract — A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first order statistic of the random finite set of targets, in time. At present, there is no closed form solution to the PHD recursion. This work shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed form recursions for propagating the means, covariances and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters. Index Terms — Multi-target tracking, optimal filtering, point
详细资料
- 文献种类: Journal Article
- 期刊名称: IEEE Trans. SP
- 期卷页: 2006年 4091-4104页
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