FILTERS FOR SPATIAL POINT PROCESSES

被引：35

作者：

Singh, Sumeetpal S. ^{[1
]}

Vo, Ba-Ngu ^{[2
]}

Baddeley, Adrian ^{[3
,4
]}

Zuyev, Sergei ^{[5
]}

机构：

[1] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic, Australia

[3] Univ Western Australia, Sch Math & Stat, Perth, WA 6009, Australia

[4] CSIRO Math & Informat Sci, Perth, WA, Australia

[5] Chalmers Univ Technol, Dept Math Sci, S-41296 Gothenburg, Sweden

来源：

SIAM JOURNAL ON CONTROL AND OPTIMIZATION | 2009年 / 48卷 / 04期

基金：

澳大利亚研究理事会;

关键词：

PHD filter; target tracking; hidden point process inference; online filtering; Poisson point process prior; Gauss-Poisson point process;

D O I：

10.1137/070710457

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the general problem of estimating a "hidden" point process X, given the realization of an "observed" point process Y (possibly defined in different spaces) with known joint distribution. We characterize the posterior distribution of X under marginal Poisson and Gauss-Poisson priors and when the transformation from X to Y includes thinning, displacement, and augmentation with extra points. These results are then applied in a filtering context when the hidden process evolves in discrete time in a Markovian fashion. The dynamics of X considered are general enough for many target tracking applications.

引用

页码：2275 / 2295

页数：21

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