3D Indoor Localization using 5G-based Particle Filtering and CAD Plans

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

IEEE Signal Processing Letters ◽

10.1109/lsp.2019.2945402 ◽

2019 ◽

Vol 26 (12) ◽

pp. 1773-1777 ◽

Cited By ~ 5

Author(s):

Parvin Malekzadeh ◽

Arash Mohammadi ◽

Mihai Barbulescu ◽

Konstantinos N. Plataniotis

Keyword(s):

Particle Filtering ◽

Indoor Localization ◽

Low Energy ◽

Bluetooth Low Energy

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Indoor Mapping and Localization for a Smart Wheelchair Using Measurements of Ambient Magnetic Fields

Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems ◽

10.1115/dscc2015-9915 ◽

2015 ◽

Cited By ~ 1

Author(s):

Anthony T. Trezza ◽

Nurali N. Virani ◽

Kelilah L. Wolkowicz ◽

Jason Z. Moore ◽

Sean N. Brennan

Keyword(s):

Magnetic Fields ◽

Particle Filtering ◽

Indoor Localization ◽

Daily Lives ◽

Motion Models ◽

Working Environments ◽

Kinematic Models ◽

Mapping Techniques ◽

Reliable Solution ◽

Smart Wheelchair

Freedom of mobility is a crucial aspect of our daily lives. Consequently, engineering solutions for mobility, including smart wheelchairs, are becoming increasingly important for those with disabilities. However, the lack of a reliable solution for indoor localization has affected the pace of research in this direction. GPS signals cannot be measured indoors and environment modifications for wheelchair localization can be expensive and intrusive. This research explores the feasibility of using ambient magnetic fields for indoor localization by exploiting the spatial non-uniformity due to ferromagnetic objects in ordinary working environments. A non-parametric density estimation technique was developed to build magnetic field maps. This approach is compared to an existing regression technique. Two different approximate kinematic models for the wheelchair are presented and implemented in a particle-filtering framework. Finally, the efficacy of these mapping techniques and motion models, including and excluding odometry information, are compared via tracking experiments conducted with a smart wheelchair.

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Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Sensors ◽

10.3390/s20102818 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2818

Author(s):

Ruolin Guo ◽

Danyang Qin ◽

Min Zhao ◽

Xinxin Wang

Keyword(s):

Particle Filtering ◽

Wireless Local Area Network ◽

Indoor Localization ◽

Local Area Network ◽

Dead Reckoning ◽

Local Area ◽

Area Network ◽

Map Construction ◽

Indoor Radio ◽

Radio Map

The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

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Indoor Localization by using Particle Filtering Approach with Wireless Sensor Nodes

Journal of Communications Software and Systems ◽

10.24138/jcomss.v9i1.159 ◽

2013 ◽

Vol 9 (1) ◽

pp. 74

Author(s):

Hakan Koyuncu ◽

Ahmet Çevik

Keyword(s):

Particle Filtering ◽

Indoor Localization ◽

Propagation Model ◽

Sensor Nodes ◽

Wireless Sensor ◽

Distance Measurements ◽

Wireless Sensor Nodes ◽

Object Distance ◽

Object Locations ◽

Current Estimation

Jennic type wireless sensor nodes are utilized together with a novel particle filtering technique for indoor localization. Target objects are localized with an accuracy of around 0.25 meters. The proposed technique introduces a new particle generation and distribution technique to improve current estimation of object positions. Particles are randomly distributed around the object in the sensing area within a circular strip of 2 STD of object distance measurements. Particle locations are related to object locations by using Gaussian weight distribution methods. Object distances from the transmitters are determined by using received RSSI values and ITU-R indoor propagation model. Measured object distances are used together with the particle distances from the transmitters to predict the object locations.

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Dynamic searching particle filtering scheme for indoor localization in wireless sensor network

2012 9th Workshop on Positioning, Navigation and Communication ◽

10.1109/wpnc.2012.6268740 ◽

2012 ◽

Cited By ~ 3

Author(s):

Yubin Zhao ◽

Yuan Yang ◽

Marcel Kyas

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Particle Filtering ◽

Indoor Localization ◽

Wireless Sensor

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UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

Journal of Sensors ◽

10.1155/2021/1383767 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Xiaoguo Zhang ◽

Yujin Kuang ◽

Haoran Yang ◽

Hang Lu ◽

Yuan Yang

Keyword(s):

Particle Filtering ◽

Sequential Monte Carlo ◽

Indoor Localization ◽

Indoor Positioning ◽

Research Field ◽

Ultra Wideband ◽

Localization Algorithm ◽

Application Potential ◽

Non Gaussian ◽

Searching Ability

With the increasing application potential of indoor personnel positioning, ultra-wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non-Line-Of-Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non-Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.

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