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使用超宽带设备在无加速度测量的结构化环境中进行定位,并使用卡尔曼-布西滤波器进行速度估计。

Localization in Structured Environments with UWB Devices without Acceleration Measurements, and Velocity Estimation Using a Kalman-Bucy Filter.

作者信息

Alonge Francesco, Cusumano Pasquale, D'Ippolito Filippo, Garraffa Giovanni, Livreri Patrizia, Sferlazza Antonino

机构信息

Department of Engineering, University of Palermo, Viale delle Scienze Ed. 10, 90128 Palermo, Italy.

Faculty of Engineering and Architecture, University of Enna KORE, 94100 Enna, Italy.

出版信息

Sensors (Basel). 2022 Aug 22;22(16):6308. doi: 10.3390/s22166308.

DOI:10.3390/s22166308
PMID:36016076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9415939/
Abstract

In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be suitable for use in environments where the GPS signal is not present or where it might fail. The second one is that no accelerometer measurements are needed for the localization task. Moreover, to deal with the velocity estimation problem, a suitable Kalman-Bucy filter is designed and it is compared, experimentally, with a particle filter by showing the features of the two algorithms in order to be used in a localization context. Additionally, further experimental tests are carried out on a suitable developed test setup in order to confirm the goodness of the proposed approach.

摘要

在这项工作中,提出了一种用于基于超宽带(UWB)距离定位系统中速度和位置估计的新颖方案。所建议的估计策略能够克服定位系统中通常遇到的两个主要问题。第一个问题是它适用于不存在GPS信号或GPS信号可能失效的环境。第二个问题是定位任务不需要加速度计测量。此外,为了处理速度估计问题,设计了一种合适的卡尔曼 - 布西滤波器,并通过展示两种算法的特性在实验上与粒子滤波器进行比较,以便在定位环境中使用。此外,在合适的开发测试装置上进行了进一步的实验测试,以确认所提方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/cd225526abc2/sensors-22-06308-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/96a5cb71aab0/sensors-22-06308-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/2e3fb4c6a1d8/sensors-22-06308-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/52ff8e4d1a7a/sensors-22-06308-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/27564ebd3392/sensors-22-06308-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/fe2250d5d261/sensors-22-06308-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/5d6f5ea15381/sensors-22-06308-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/16c5f45533c8/sensors-22-06308-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/cd225526abc2/sensors-22-06308-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/49f6b68ec465/sensors-22-06308-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/16946be2b115/sensors-22-06308-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/f410089e3b7c/sensors-22-06308-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/e312433c95c3/sensors-22-06308-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/96a5cb71aab0/sensors-22-06308-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/2e3fb4c6a1d8/sensors-22-06308-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/52ff8e4d1a7a/sensors-22-06308-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/27564ebd3392/sensors-22-06308-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/fe2250d5d261/sensors-22-06308-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/5d6f5ea15381/sensors-22-06308-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/16c5f45533c8/sensors-22-06308-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c6/9415939/cd225526abc2/sensors-22-06308-g011.jpg

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