scholarly journals Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xinlong Jiang ◽  
Yiqiang Chen ◽  
Junfa Liu ◽  
Dingjun Liu ◽  
Yang Gu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Indoor Localization ◽  
High Accuracy ◽  
Motion Sensors ◽  
Fusion Model ◽  
Indoor Location ◽  
Smooth Tracking ◽  
Error Accumulation ◽  
Learning Machine

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.

RFID Indoor Localization Using Master-Slave Reference Tags Scheme for Manufacturing Environment

2014 ◽  
Author(s):  
Shilong Zhang ◽  
Quan Liu ◽  
Wenjun Xu ◽  
Zaiqun Liu
Keyword(s):  
Real Time ◽  
Manufacturing Process ◽  
Indoor Localization ◽  
Physical Object ◽  
Location Information ◽  
Localization Algorithm ◽  
System Load ◽  
Indoor Location ◽  
Time Performance ◽  
Reference Tags

In manufacturing process, the indoor location information of physical object is an essential part in storage and transport link. The efficient perception of indoor location is able to significantly reduce the system load and also improves its real-time performance. In this paper, a novel RFID indoor localization algorithm using Master-Slave reference tags scheme (MSRT) is presented. The algorithm divides the sensing area into several subspaces with master reference tags to realize rough location. In each subspaces, slave reference tags are used to perform partial location. A set of experiments have been conducted and the results demonstrate that the proposed method can reduce system redundancy and server load without decrement of accuracy.

scholarly journals High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xianmin Li ◽  
Zihong Yan ◽  
Linyi Huang ◽  
Shihuan Chen ◽  
Manxi Liu
Keyword(s):  
Mobile Robot ◽  
Visible Light ◽  
Real Time ◽  
Indoor Localization ◽  
Visible Light Communication ◽  
High Accuracy ◽  
Market Potential ◽  
Location Based Services ◽  
Positioning System ◽  
Robot Positioning

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.

scholarly journals Indoor Navigation Using Particle Filter and Sensor Fusion

2012 ◽  
Vol 19 (2) ◽  
pp. 31-40
Author(s):  
Lukas Köping ◽  
Thomas Mühsam ◽  
Christian Ofenberg ◽  
Bernhard Czech ◽  
Michael Bernard ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Particle Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Localization System ◽  
Multiple Sensor ◽  
Large Building

Abstract In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

scholarly journals An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6842
Author(s):  
Junhai Luo ◽  
Zhiyan Wang ◽  
Yanping Chen ◽  
Man Wu ◽  
Yang Yang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Sensor Fusion ◽  
Tracking Accuracy ◽  
Fusion Model ◽  
Unscented Transform ◽  
Time Performance ◽  
Unscented Particle Filter ◽  
Distributed Fusion

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

scholarly journals Indoor Localization Using Deep-Learning and Smartphone

2019 ◽  
pp. 40-49
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Indoor Localization ◽  
Learning Phase ◽  
Second Phase ◽  
Indoor Location ◽  
Online Processing ◽  
Offline Learning ◽  
Proposed Model ◽  
Matching Techniques

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. In the building where the GPS is not available, this project can be utilized. Indoor localization based on image matching techniques related to deep learning was achieved in a hard environment. So, if it wanted to raise the precision of indoor classification, the number of image dataset of the indoor environment should be as large as possible to satisfy and cover the underlying area. In this work, a smartphone camera is used to build the image-based dataset of the investigated building. In addition, captured images in real time are taken to be processed with the proposed model as a test set. The proposed indoor localization includes two phases the first one is the offline learning phase and the second phase is the online processing phase. In the offline learning phase, here we propose a convolutional neural network (CNN) model that sequences the features of image data from some classis's dataset composed with a smartphone camera. In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appoint the expected indoor location. The proposed system has been tested over various experiments and the obtained experimental results show that the accuracy of the prediction is almost 98.0%.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

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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