Position Vectors Based Efcient Indoor Positioning System

With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.

Barom etric altim eter in wireless com m unication network indoor positioning system

A differential barometric altimetry technology based on the digital pressure sensors is put forward by using the existing mobile phone base station as reference. The height of known base sta- tion is precise. The pressure and temperature of the known base station is measured by sensors and transmitted to users. The absolute height value of user will be calculated by combining the baromet- ric pressure values and temperature values from the base station with the locally measured values. In order to decrease system errors caused by inconsistency between the measured pressure value at base station and the locally measured pressure value, weights correction is applied based on multiple reference stations. The calculated height value is accurate due to eliminating the measured errors caused by irregular changes of atmospheric pressure, with the error less than 1 m. Resolution of ele- vation positioning depends upon the resolution of the pressure sensor, the relationship between which is approximately linear. When the resolution of sensor is 0.01 hPa, the resolution of elevation positioning is about 0. 1 m. In addition, the data frame format at base station is designed in this arti- cle. Experimental results show that the method is accurate, reliable, stable and has the ability to distinguish floors and stair steps.

Novel indoor positioning system based on ultra-wide bandwidth

To tackle challenges such as interference and poor accuracy of indoor positioning systems,a novel scheme based on ultra-wide bandwidth(UWB)technology is proposed.First,we illustrate a distance measuring method between two UWB devices.Then,a Taylor series expansion algorithm is developed to detect coordinates of the mobile node using the location of anchor nodes and the distance between them.Simulation results show that the observation error under our strategy is within 15 cm,which is superior to existing algorithms.The final experimental data in the hardware system mainly composed of STM32 and DW1000 also confirms the performance of the proposed scheme.

Optimization of MAC algorithm based on IEEE 802.15.4 in indoor positioning system

The mobility of the targets asks for high requirements of the locating speed in indoor positioning systems.The standard medium access control(MAC)algorithm will often cause lots of packet conflicts and high transmission delay if multiple users communicate with one beacon at the same time,which will severely limit the speed of the system.Therefore,an optimized MAC algorithm is proposed based on channel reservation to enable users to reserve beacons.A frame threshold is set to ensure the users with shorter data frames do not depend on the reservation mechanism,and multiple users can achieve packets switching with relative beacon in a fixed sequence by using frequency division multiplexing technology.The simulation results show that the optimized MAC algorithm proposed in this paper can improve the positioning speed significantly while maintaining the positioning accuracy.Moreover,the positioning accuracy can be increased to a certain extent if more channel resources can be obtained,so as to provide effective technical support for the location and tracking applications of indoor moving targets.

WiFi Indoor Positioning and Tracking Algorithm Based on Compressive Sensing and Sage-Husa Adaptive Kalman Filter

Aiming at the problem that the positioning accuracy of WiFi indoor positioning technology based on location fingerprint has not reached the requirements of practical application, a WiFi indoor positioning and tracking algorithm combining adaptive affine propagation (AAPC), compressed sensing (CS) and Kalman filter is proposed. In the off-line phase, AAPC algorithm is used to generate clustering fingerprints with optimal clustering effect performance;In the online phase, CS and nearest neighbor algorithm are used for position estimation;Finally, the Kalman filter and physical constraints are combined to perform positioning and tracking. By collecting a large number of real experimental data, it is proved that the developed algorithm has higher positioning accuracy and more accurate trajectory tracking effect.

Optimizing artificial neural network-based indoor positioning system using genetic algorithm

The Global Positioning System(GPS)is expected to play an integral role in the development of digital earth;however,the GPS cannot provide positioning information in regions where a majority of the population spends their time,that is,in urban and indoor environments.Hence,alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital earth.Wi-Fi-based positioning systems(WPS)stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor environments.The WPS-based fingerprinting is the most widely adopted technique for position determination,but its accuracy is lower than that of techniques such as time of arrival and angle of arrival.Improving the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi signals.Here,a novel server-based,genetic-algorithm-optimized,cascading artificial neural network-based positioning model is presented.The model is tested in 2D and 3D indoor environments under varying conditions.The model is thoroughly investigated on a real Wi-Fi network,and its accuracy is found to be better than that of other well-known techniques.A mean accuracy of 1.9 m is achieved with 87%of the distance error within the range of 0-3 m.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor Positioning

In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases.

Research Status and Trends of Indoor Positioning and Navigation Technology in China

As an essential component of future comprehensive Positioning,Navigation,and Timing(PNT)system,indoor positioning technology has extensive application demands,making it a focal point of attention in both academia and industry.This article comprehensively reviews the research status of indoor positioning technology in China,with a focus on highlighting representative achievements and application validations from major research institutions in recent years.It addresses the challenges and issues faced in promotion and application of large-scale,high-precision indoor positioning.Furthermore,a universal and seamless indoor-outdoor positioning system architecture is proposed,along with a technical roadmap and key technologies to achieve this architecture.Finally,an analysis and outlook on future technological trends are presented.

Indoor Positioning System Using Axis Alignment and Complementary IMUs for Robot Localization

An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the accelerometer.The integral error of velocity is eliminated by a new subsection calculation method.Two complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement results.Secondly,an orientation estimation based on a fusion filter for the steering motion is proposed.Experiments show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.

Toward the implementation of a universal angle-based optical indoor positioning system

There is an emerging market today for indoor positioning systems capable of working alongside global navigation satellite systems, such as the global positioning system, in indoor environments. Many systems have been proposed in the literature but all of them have fundamental flaws that hold them back from widescale implementation. We review angle-of-arrival （AOA） and angle-difference- of-arrival （ADOA） optical indoor positioning systems which have been proven to be robust, accurate, and easily implementable. We build an AOA/ADOA optical indoor positioning system out of a simple commercial high-speed camera and white light light emitting diodes （LEDs） which operate over a working area of 1 m3, and compare its performance to other indoor positioning methods. The AOA and ADOA systems achieve positioning with low errors of 1.2 and 3.7 cm, respectively.

Fingerprint Database Updating Using Crowdsourcing in Indoor Bluetooth Positioning System

Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will result in the decline of positioning accuracy.The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating.To address this problem,this paper presents an adaptive fingerprint database updating approach.First,the crowdsourced data including the Bluetooth Received Signal Strength(RSS)sequences and the speed and heading of the pedestrian were recorded.Second,the recorded crowdsourced data were fused by the Kalman Filtering(KF),and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints.Third,after enough candidate fingerprints were obtained at each Reference Point(RP),the Density⁃based Spatial Clustering of Applications with Noise(DBSCAN)approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise,and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP.Finally,the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations,the fingerprint⁃based Bluetooth positioning accuracy can be effectively improved.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

Analysis of Geo Positioning System Using RF &DA

Indoor positioning systems (IPSs) have been intended to provide position information of persons and devices. Higher user percentage of handheld devices such as tablets or mobile phones had led to the development of a number of indoor positioning systems. In this research a work on a real time portable RFID indoor positioning device such as on smartphone will be performed. The personal networks will be designed to meet the users’ needs and interconnect users’ devices equipped with different communications technologies in various places to form one network for better result. Radio frequency identification (RFID) with directional antenna has proved its potential for locating objects in indoor environment. Hence, the proposed device idea will be used to exploit various unknown locations in an indoor environment such as college campus;this interpretation will rely on Wireless LAN, Received Signal Strength values from Access Points (AP) in specific mentioned arenas;these APs will be monitored constantly by RFID with directional antenna (DA) and handheld devices. For obtaining the better results from existing devices, algorithms of Range Estimation are proposed, which can be used on various handheld devices for locating indoor objects.

Research and Development of Indoor Positioning

Indoor positioning systems have been sufficiently researched to provide location information of persons and devices.This paper is focused on the current research and further development of indoor positioning.The standard evolution and industry development are summarized.There are various positioning systems according to the scenarios,cost and accuracy.However,there is a basic positioning system framework including information extraction,measurement and calculation.In particular,the detailed positioning technologies mainly including cellular positioning and Local Area Network(LAN) positioning are listed and compared to provide a reference for practical applications.Finally,we summarize the challenges of indoor positioning and give a3-phase evolution route.

A Novel Indoor Positioning Framework

Current positioning systems are primarily based on the Global Positioning System(GPS).Although the GPS is accurate within 10 m,it is mainly used for outdoor positioning services(Location-Based Service;LBS).However,since satellite signals cannot penetrate buildings,indoor positioning has always been a blind spot for satellite signals.As indoor positioning applications are extensive with high commercial values,they have created a competitive niche in themarket.Existing indoor positioning technologies are unable to achieve less than 10 cmaccuracy except for the UltraWide Band(UWB)technology.On the other hand,the Bluetooth protocol achieves an accuracy of 1 to 2m.In this work,we use Bluetooth wireless signals to build a novel indoor positioning framework to avoid the high manufacturing costs involved in the UWB technology.The Bluetooth signals are combined with the results from artificial intelligence algorithms to improve accuracy.During laboratory indoor location tracking,the accuracy rate is 96%,which provides effective indoor tracking for the movement of people.

Research on Indoor Passive Positioning Technology Based on WiFi

In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.

5G Ultra-Dense Network Fingerprint Positioning Method Based on Matrix Completion

The problem of high-precision indoor positioning in the 5G era has attracted more and more attention.A fingerprint location method based on matrix completion(MC-FPL)is proposed for 5G ultradense networks to overcome the high costs of traditional fingerprint database construction and matching algorithms.First,a partial fingerprint database constructed and the accelerated proximal gradient algorithm is used to fill the partial fingerprint database to construct a full fingerprint database.Second,a fingerprint database division method based on the strongest received signal strength indicator is proposed,which divides the original fingerprint database into several sub-fingerprint databases.Finally,a classification weighted K-nearest neighbor fingerprint matching algorithm is proposed.The estimated coordinates of the point to be located can be obtained by fingerprint matching in a sub-fingerprint database.The simulation results show that the MC-FPL algorithm can reduce the complexity of database construction and fingerprint matching and has higher positioning accuracy compared with the traditional fingerprint algorithm.