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 Unsupervised Indoor Positioning System Based on Environmental Signatures

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 327
Author(s):  
Pan Feng ◽  
Danyang Qin ◽  
Min Zhao ◽  
Ruolin Guo ◽  
Teklu Berhane
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Convergence Time ◽  
Positioning System ◽  
Indoor Environments ◽  
Test Results ◽  
Positioning Errors ◽  
Localization And Mapping ◽  
Indoor Positioning System ◽  
Unconventional Method

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.

scholarly journals Indoor Localization System Using Wireless Sensor Network

2018 ◽  
pp. 29-38
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Wide Band ◽  
Human Movement ◽  
Indoor Positioning ◽  
Positioning System ◽  
Wireless System ◽  
Anchor Nodes ◽  
Indoor Positioning System ◽  
Better Than

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5776
Author(s):  
Zhongfeng Zhang ◽  
Minjae Lee ◽  
Seungwon Choi
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Software Radio ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Training Dataset ◽  
Positioning System ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Indoor Positioning System

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

scholarly journals A REVIEW ON INDOOR LOCALIZATION WITH INTERNET OF THINGS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 121-128
Author(s):  
C. Basri ◽  
A. Elkhadimi
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Location Based Services ◽  
Positioning System ◽  
Resources Management ◽  
Indoor Positioning System

Abstract. The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.

scholarly journals THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 379-383
Author(s):  
J. Liu ◽  
C. Jiang ◽  
Z. Shi
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Localization Algorithm ◽  
External Device ◽  
Localization System ◽  
Filtering Algorithm ◽  
Coordinate Conversion ◽  
Indoor Positioning System

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

scholarly journals Indoor Positioning System Based on Standardizing Waveform Tendency

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaohua Li ◽  
Ge Yu
Keyword(s):  
Environmental Changes ◽  
Indoor Localization ◽  
Real Life ◽  
Dynamic Environment ◽  
Indoor Positioning ◽  
Positioning System ◽  
Kernel Extreme Learning Machine ◽  
Computationally Intensive ◽  
Learning Machine ◽  
Indoor Positioning System

Estimating the indoor position of users in commercial buildings remains a significant challenge to date. Although the WiFi-based indoor localization has been widely explored in many works by employing received signal strength (RSS) patterns as the features, they usually lead to inaccurate results as the RSS could be easily affected by the indoor environmental dynamics. Besides, existing methods are computationally intensive, which have a high time consumption that makes them unsuitable for real-life applications. In order to deal with those issues, we propose to use standardizing waveform tendency (SWT) of RSS for indoor positioning. We show that the proposed SWT is robust to the noise generated by the dynamic environment. We further develop a novel smartphone indoor positioning system by integrating SWT and kernel extreme learning machine (KELM) algorithm. Extensive real-world positioning experiments are conducted to demonstrate the superiority of our proposed model in terms of both positioning accuracy and robustness to environmental changes when comparing with state-of-the-art baselines.

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