scholarly journals JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2722
Author(s):  
Lu Yin ◽  
Pengcheng Ma ◽  
Zhongliang Deng
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
High Accuracy ◽  
Feature Representation ◽  
Location Based Services ◽  
High Dimensional ◽  
Proposed Model ◽  
Extraction Algorithm ◽  
Positioning Method ◽  
Fingerprint Data

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on received signal strength (RSS), is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm—named joint denoising auto-encoder (JDAE)—which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on the UJIIndoorLoc dataset and the Tampere dataset, the experimental results show that the proposed model increases the positioning accuracy dramatically compared with other existing methods.

scholarly journals JLGBMLoc: A Novel High-precision Indoor Localization Method Based on LightGBM

2021 ◽  
Author(s):  
Lu Yin ◽  
Pengcheng Ma ◽  
Zhongliang Deng
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
High Accuracy ◽  
Feature Representation ◽  
Location Based Services ◽  
High Dimensional ◽  
Proposed Model ◽  
Extraction Algorithm ◽  
Positioning Method ◽  
Fingerprint Data

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

A Method of Indoor Localization Based on BLE4.0 Protocol of Using Beacons

2015 ◽  
Vol 740 ◽  
pp. 765-768
Author(s):  
He Shan Bian ◽  
Zhao Hui Li ◽  
Fang Zhao
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Indoor Localization ◽  
Signal Strength ◽  
Mobile Terminal ◽  
High Accuracy ◽  
Low Energy ◽  
Energy Technology ◽  
Bluetooth Low Energy ◽  
Distance Model

In this paper we discuss our attempt to solve the problem of HAIP(High Accuracy Indoor Position) by using BLE4.0(Bluetooth Low Energy). According to previous research, Wi-Fi Positioning has mainly faced some big challenges. Accuracy is deteriorated by directional handset antennas, which affect the relative AP signal strength; Practical maximum reachable accuracy is 3-10 meters depending on environment; Wi-Fi activities is a big consumption of battery on Mobile Terminal; Now, The Bluetooth Low Energy technology is getting mature. In this paper, we use Bluetooth low energy on iOS device to solve the problem of high accuracy indoor position. In the data-preprocessing step, we use Kalman filter to process the RSSI. In the transition step of RSSI to Distance, we propose a novelty method to adjust the parameters of Log-Distance model dynamically and adaptively according to diagonal beacons’ measurement. We implement our technique and algorithm on iOS device with iOS7.0 SDK. The result shows that error reduced to 0.5m-1.2m range depending on the distance, achieved smaller power consumption.

scholarly journals SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881563 ◽  
Author(s):  
Jie Wei ◽  
Fang Zhao ◽  
Haiyong Luo
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Based Services ◽  
Shopping Mall ◽  
Time Stamp ◽  
Localization Algorithm ◽  
Variation Pattern ◽  
Localization Accuracy ◽  
User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization 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 A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals A Pairwise SSD Fingerprinting Method of Smartphone Indoor Localization for Enhanced Usability

2019 ◽  
Vol 11 (5) ◽  
pp. 566 ◽  
Author(s):  
Fan Yang ◽  
Jian Xiong ◽  
Jingbin Liu ◽  
Changqing Wang ◽  
Zheng Li ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Indoor Localization ◽  
Signal Strength ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Positioning Errors ◽  
Area Of Interest ◽  
Fingerprinting Method ◽  
Strength Difference ◽  
Radio Map

Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones

scholarly journals Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772215 ◽  
Author(s):  
Juan Manuel Castro-Arvizu ◽  
Jordi Vilà-Valls ◽  
Ana Moragrega ◽  
Pau Closas ◽  
Juan A Fernandez-Rubio
Keyword(s):  
Indoor Localization ◽  
Channel Model ◽  
Real Life ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Based Services ◽  
Model Parameters ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Shadow Fading

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.

High-Accuracy Visible Light Positioning Method Based on Received Signal Strength Indicator

2018 ◽  
Vol 45 (3) ◽  
pp. 0306002
Author(s):  
叶子蔚 Ye Ziwei ◽  
叶会英 Ye Huiying ◽  
聂翔宇 Nie Xiangyu ◽  
习小玉 Xi Xiaoyu
Keyword(s):  
Visible Light ◽  
Signal Strength ◽  
Received Signal Strength ◽  
High Accuracy ◽  
Received Signal Strength Indicator ◽  
Positioning Method

scholarly journals Location Identification Methods for IOT Devices using Bluetooth Low Energy (BLE)

2019 ◽  
Vol 9 (1) ◽  
pp. 4257-4260
Keyword(s):  
Response Time ◽  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Low Energy ◽  
Identification Methods ◽  
Positioning Method ◽  
Iot Devices ◽  
Closed Environment ◽  
Environment Condition

Indoor localization is a positioning method that used for closed environment condition. Since Internet of Things or IOT devices come with small size microcontroller units, positioning algorithms are studied and three of them are tested based on three parameters which are accuracy, algorithms response time and programming bit size. In this project, three Radio Signal Strength Indicator or RSSI based positioning algorithms are used to get the results which are trilateration, fingerprinting and proximity. Based on the data collected, proximity algorithm gives the highest accuracy, lowest response time, and lowest programming bit size. This project can be improved by sending the location of IOT devices to cloud so that it can be track by other IOT devices.

scholarly journals Improved indoor positioning based on range-free RSSI fingerprint method

2020 ◽  
Vol 10 (1) ◽  
pp. 23-28
Author(s):  
Marcin Uradzinski ◽  
Hang Guo ◽  
Min Yu
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Wireless Sensor ◽  
Positioning Accuracy ◽  
Gps Positioning ◽  
Zigbee Technology ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Range Free

AbstractAs the development of modern science and technology, LBS and location-aware computing are increasingly important in the practical applications. Currently, GPS positioning system is a mature positioning technology used widely, but signals are easily absorbed, reflected by buildings, and attenuate seriously. In such situation, GPS positioning is not suitable for using in the indoor environment.Wireless sensor networks, such as ZigBee technology, can provide RSSI (received signal strength indicator) which can be used for positioning, especially indoor positioning, and therefore for location based services (LBS).The authors are focused on the fingerprint database method which is suitable for calculating the coordinates of a pedestrian location. This positioning method can use the signal strength indication between the reference nodes and positioning nodes, and design algorithms for positioning. In the wireless sensor networks, according to whether measuring the distance between the nodes in the positioning process, the positioning modes are divided into two categories which are range-based and range-free positioning modes. This paper describes newly improved indoor positioning method based on RSSI fingerprint database, which is range-free.Presented fingerprint database positioning can provide more accurate positioning results, and the accuracy of establishing fingerprint database will affect the accuracy of indoor positioning. In this paper, we propose a new method about the average threshold and the effective data domain filtering method to optimize the fingerprint database of ZigBee technology. Indoor experiment, which was conducted at the University of Warmia and Mazury, proved that the distance achieved by this system has been extended over 30 meters without decreasing the positioning accuracy. The weighted nearest algorithm was chosen and used to calculate user’s location, and then the results were compared and analyzed. As a result, the positioning accuracy was improved and error did not exceed 0.69 m. Therefore, such system can be easily applied in a bigger space inside the buildings, underground mines or in the other location based services.

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