scholarly journals Indoor Localization Based on Bluetooth

2019 ◽  
Vol 7 (2) ◽  
pp. 87 ◽  
Author(s):  
Jun Yin ◽  
Fengchun Yin
Keyword(s):  
Indoor Localization ◽  
Mobile Terminal ◽  
Indoor Positioning ◽  
Detection Algorithm ◽  
Indoor Environments ◽  
Construction Time ◽  
Indoor Scenes ◽  
Fingerprint Database ◽  
To Receive ◽  
Transmission Speed

Global positioning system (GPS) has been widely used in positioning, vehicle navigation and other environments. However, in indoor environments, it can not achieve accurate positioning because of the weak signal through the wall. In other words, more appropriate techniques are needed in indoor scenes. Bluetooth technology has attracted more and more attention due to its advantages of low power consumption, wide coverage and fast transmission speed. Bluetooth-based indoor positioning refers to the indoor positioning technology that uses mobile terminal to receive Bluetooth signals from multiple Bluetooth devices, and calculates the location information of mobile terminal through the received information, so as to achieve high-precision positioning.In this paper, an effective optimal location algorithm is proposed. Firstly, the outlier detection algorithm is improved to remove the interference of abnormal data on the positioning accuracy; then, different filtering algorithms are used to process the received fingerprint information to ensure the accuracy of the fingerprint database establishment stage, and reduce the unnecessary construction time; finally, the average position is calculated by the average fingerprint data and judged the user's area.

scholarly journals Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
University Campus ◽  
Sources Of Information ◽  
Visual Place Recognition ◽  
Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

scholarly journals An efficient crowd-sourcing-based approach for fingerprint database updating

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985851
Author(s):  
Xi Liu ◽  
Jian Cen ◽  
Yiju Zhan ◽  
Chengpei Tang
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Primary Concern ◽  
Indoor Environments ◽  
Crowd Sourcing ◽  
Static Data ◽  
Decision Methods ◽  
Fingerprint Database ◽  
Update Frequency

Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

scholarly journals An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4420 ◽  
Author(s):  
Lu Huang ◽  
Xingli Gan ◽  
Baoguo Yu ◽  
Heng Zhang ◽  
Shuang Li ◽  
...  
Keyword(s):  
Carrier Phase ◽  
Indoor Environment ◽  
Satellite System ◽  
Indoor Positioning ◽  
Integer Ambiguity ◽  
Positioning System ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Static Conditions ◽  
Fingerprint Database

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.

scholarly journals A novel indoor localization scheme based on refined fingerprint-based autoencoder network

2018 ◽  
Vol 189 ◽  
pp. 03017
Author(s):  
Junhui Mei ◽  
Juntong Xi
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Access Point ◽  
Indoor Environments ◽  
Location Based Service ◽  
Accuracy Improvement ◽  
Positioning Systems ◽  
Localization Scheme ◽  
Environmental Properties ◽  
Indoor Scenes

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

scholarly journals Exploring Fast Fingerprint Construction Algorithm for Unmodulated Visible Light Indoor Localization

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7245
Author(s):  
Chenqi Shi ◽  
Xinyv Niu ◽  
Tao Li ◽  
Sen Li ◽  
Chanjuan Huang ◽  
...  
Keyword(s):  
Visible Light ◽  
Indoor Localization ◽  
Experimental Results ◽  
Indoor Environments ◽  
Theoretical Derivation ◽  
Positioning Accuracy ◽  
Construction Algorithm ◽  
Rapid Construction ◽  
Fingerprint Database

The study of visible light indoor position has received considerable attention. The visible light indoor position has problems such as deployment difficulty and high cost. In our system, we propose a new fingerprint construction algorithm to simplify visible light indoor position. This method can realize the rapid construction of a visible fingerprint database and prove that the fingerprint database can be used repeatedly in different environments. We proved the theoretical feasibility of this method through theoretical derivation. We carried out extensive experiments in two classic real indoor environments. Experimental results show that reverse fingerprinting can be achieved. In 95% of cases, the positioning accuracy can be guaranteed to be less than 10 cm.

scholarly journals A Review on Indoor Positioning Technologies and its Principles

2021 ◽  
Vol 9 (VII) ◽  
pp. 494-501
Author(s):  
Pradyumna C
Keyword(s):  
Indoor Localization ◽  
Indoor Positioning ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Daily Lives ◽  
Positioning Systems ◽  
Wide Range ◽  
Outdoor Environments ◽  
Location Detection ◽  
Pass Through

This paper aims to provide the reader with a review of the main technologies present in the literature to solve the indoor localization problem that is indoor positioning without GPS. Location detection has been implemented very successfully in outdoor environments using GPS technology. GPS has had a great impact on our daily lives by supporting a large number of applications. However, in indoor environments, the availability of GPS or equivalent satellite-based positioning systems is limited due to the lack of line of sight and attenuation of the GPS signal when they pass through walls. The goal of this paper is to provide a technical perspective on indoor positioning systems, including a wide range of technologies and methods.

scholarly journals A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3233 ◽  
Author(s):  
Xiaochao Dang ◽  
Xuhao Tang ◽  
Zhanjun Hao ◽  
Yang Liu
Keyword(s):  
Indoor Localization ◽  
Indoor Positioning ◽  
Location Based Service ◽  
Channel State ◽  
Position Information ◽  
Fine Grained ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Device Free ◽  
Limited Accuracy

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

scholarly journals CSI Amplitude Fingerprinting for Indoor Localization with Dictionary Learning

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1164
Author(s):  
Wen Liu ◽  
Xu Wang ◽  
Zhongliang Deng
Keyword(s):  
Dictionary Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Indoor Positioning ◽  
Reconstruction Error ◽  
Test Point ◽  
Training Data ◽  
Support Vector ◽  
Location Services ◽  
Indoor Scenes

With the rapid growth of the demand for location services in the indoor environment, fingerprint-based indoor positioning has attracted widespread attention due to its high-precision characteristics. This paper proposes a double-layer dictionary learning algorithm based on channel state information (DDLC). The DDLC system includes two stages. In the offline training stage, a two-layer dictionary learning architecture is constructed for the complex conditions of indoor scenes. In the first layer, for the input training data of different regions, multiple sub-dictionaries are generated corresponding to learning, and non-coherent promotion items are added to emphasize the discrimination between sparse coding in different regions. The second-level dictionary learning introduces support vector discriminant items for the fingerprint points inside each region, and uses Max-margin to distinguish different fingerprint points. In the online positioning stage, we first determine the area of the test point based on the reconstruction error, and then use the support vector discriminator to complete the fingerprint matching work. In this experiment, we selected two representative indoor positioning environments, and compared the DDLC with several existing indoor positioning methods. The results show that DDLC can effectively reduce positioning errors, and because the dictionary itself is easy to maintain and update, the characteristic of strong anti-noise ability can be better used in CSI indoor positioning work.

scholarly journals Indoor Localization Based on Optimized KNN

2020 ◽  
Vol 5 (2) ◽  
pp. 34
Author(s):  
Xuanyu Zhu
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Local Area ◽  
Indoor Positioning ◽  
Area Network ◽  
Online Matching ◽  
Fingerprint Database ◽  
The Cost ◽  
Real Time Information

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the “Navigator” of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.

An improvement for the foreground recognition method using shadow removal technique for indoor environments

2017 ◽  
Vol 15 (04) ◽  
pp. 1750039 ◽  
Author(s):  
Akmalbek Abdusalomov ◽  
Taeg Keun Whangbo
Keyword(s):  
Image Enhancement ◽  
Detection Algorithm ◽  
Shadow Detection ◽  
Indoor Environments ◽  
Recognition Method ◽  
Detection Algorithms ◽  
Tracking Tasks ◽  
Indoor Scenes ◽  
Removal Technique ◽  
Moving Shadow Detection

In this paper, we present an efficient and simple shadow detection algorithm for indoor environments, as well as give a brief description on the advantages of this method. In this method, we use three types of approaches: image enhancement, chromaticity consistency, and gradient features. Multiple shadow direction is becoming an increasingly challenging task for many moving shadow detection algorithms because some objects have large self-shadows. Our system is able to achieve good performance solving spread shadow problems in indoor scenes, leading to improved foreground segmentation in surveillance scenarios. The image enhancement approach is first employed to input images to generate high-quality images for artificial light source indoor areas. Afterwards, the chromaticity information is utilized to create a mask of possible candidate shadow pixels. Subsequently, gradient features are applied to remove foreground pixels that have been incorrectly included in the mask. In comparison with existing algorithms, the proposed method can correctly detect and remove shadow pixels to identify original foreground shapes without distortion for delivering object recognition and tracking tasks.

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