GMPHD based Multi-Scan Clutter Sparsity Estimation

被引：0

作者：

Guo, Yunfei ^{[1
]}

Pan, Jinxing ^{[1
]}

Peng, Dongliang ^{[1
]}

Chen, Zhikun ^{[1
]}

Han Shentu ^{[1
]}

机构：

[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China

来源：

2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to solve the problem of multi-target tracking in clutter with unknown density, a Gaussian mixture probability hypothesis density (GMPHD) based multi-scan clutter sparsity estimation (MCSE) algorithm is proposed. First, the GMPHD filter is used to estimate the cardinality and state of the target with the clutter density in last step. Then all Measurements originated from the targets are eliminated online, which helps to reduce the effects on the clutter density estimation of target-originated measurements. Last, a multi-scan clutter sparsity estimation algorithm is proposed to update the current clutter density. Simulation results verify the effectiveness of the proposed algorithm.

引用

页码：1453 / 1458

页数：6

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