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基于多传感器观测聚类的鲁邦多传感器PHD滤波

已有 1730 次阅读 2018-8-14 16:43 |个人分类:科研笔记|系统分类:论文交流| PHD, 滤波器, 传感网, 信息融合, 多目标跟踪, PHD, PHD, PHD

论文:  A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering

作者:Tiancheng Li ;  Javier Prieto ;  Hongqi Fan ;  Juan M. Corchado

Published in: IEEE Communications Letters ( Volume: 22 , Issue: 10 , Oct. 2018 )

Page(s): 2064 - 2067


连接: https://ieeexplore.ieee.org/document/8425712/       

 

This letter presents a novel multi-sensor probability hypothesis density (PHD) filter for multi-target tracking by means of multiple or even massive sensors that are linked by a fusion center or by a peer-to-peer network. 

As the challenge we confront, little is known about the statistical properties of the sensors in terms of their measurement noise, clutter, target detection probability and even potential cross-correlation. 

Our approach converts the collection of the measurements of different sensors to a set of proxy, homologous measurements. These synthetic measurements overcome the problems of false and missing data and of unknown statistics and facilitate linear PHD updating that amounts to the standard PHD filtering with no false and missing data. 

Simulation has demonstrated the advantages and limitations of our approach in comparison to the cutting-edge multi-sensor/distributed PHD filters.


本文提出了一种新的多传感器概率假设密度(PHD)滤波器,用于集中式式或点对点分布式网络链接的多个甚至大量传感器下的多目标跟踪。 

本文主要解决的一个挑战是系统缺乏传感器的统计特性如测量噪声、杂波率、目标检测概率甚至潜在的传感器相互关联等。 

我们的方法是将不同传感器的测量数据集合、聚类转换为一组合成的代理、同源测量数据。 这些合成测量取代原始量测数据可以实现线性PHD更新,克服了虚警和漏检数据以及未知传感器统计信息的挑战。这一过程相当于一个没有漏检和虚警环境的标准PHD更新。

 仿真验证了我们的方法与当前主流的集中式多传感器/分布式PHD滤波器相比的优势和局限性。


T. Li, J. Prieto, H. Fan and J. M. Corchado, "A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering," in IEEE Communications Letters, vol. 22, no. 10, pp. 2064-2067, Oct. 2018.

doi: 10.1109/LCOMM.2018.2863387



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