Indoor Localization Techniques Research Articles

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

Experimental study of ARtag-based indoor localization techniques

Abstract To address the limitations of traditional indoor localization techniques in indoor environments, the ARtag indoor localization technique is proposed to determine the optimal recognition distance to optimize the robot’s localization performance in indoor environments. The performance of the robot at different distances is evaluated by setting up an ARtag at different distances in an indoor environment, testing and comparing the robot’s recognition accuracy and response time at these distances. The experimental results show that at a distance of 25 cm, the robot has the fastest response speed and the highest stability, with a recognition accuracy of 100% and a response time of only 0.39 ms. The ARtag indoor localization technique can significantly improve the robot’s localization performance at specific distances, which can provide a basis for optimizing the performance of indoor robotic applications.

Comparative Analysis of Filtering Techniques for AGV Indoor Localization with Ultra-Wideband Technology

This paper investigates the filtering techniques to enhance the accuracy of indoor localization for Autonomous Guided Vehicles (AGVs) using Ultra-Wideband (UWB) technology. A comprehensive comparative analysis of various filtering approaches, including the Kalman Filter (KF), Moving Average Filter (MA), Savitzky-Golay Filter (SG), Weighted Average Filter (WAF), and their combinations, are conducted. The primary focus of this paper is the integration of a Moving Average-Kalman Filter (MAKF) with an extended window size of 201. Experimental findings reveal significant performance differences among these filtering techniques. The most effective approach is the MAKF technique, achieving an accuracy of 85.13% and the lowest path deviation of 0.17 meters. Conversely, the MA exhibits the lowest accuracy at 68.83%. Notably, the WAF attains an accuracy of 72.46% but exhibits a significantly higher path deviation of 2.65 meters compared to 1.45 meters of the MA filtering technique. The proposed MAKF acknowledged for its ability to effectively reduce noise with real-time responsiveness, represents a significant advancement in AGV indoor localization techniques.

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

A comprehensive algorithm for vertical positioning in multi-building environments as an advancement in indoor floor-level detection

The proliferation of smartphones has catalyzed diverse services, mainly focusing on indoor localization to determine users’ and devices’ positions within buildings. Despite decades of exploration, the seamless integration of wireless technologies in tracking devices and users has become pivotal in various sectors, including health, industry, disaster management, building operations, and surveillance. Extensive research in laboratory and industrial settings, particularly in wireless sensor networks and robotics, has informed indoor localization techniques. This paper, referencing surveys and original literature reviews, proposes an innovative indoor location system amalgamating GPS and barometer readings. GPS identifies entry through doors, while barometer readings facilitate accurate floor-level tracking. The integration promises continuous real-time location updates, enhancing security, navigation, and emergency response. Notably, the algorithm is infrastructure-independent, relying on the smartphone’s barometer, and versatile, detecting elevator travel when Wi-Fi AP or LTE signals are available. Results indicate high accuracy, with building entry exceeding 93%, elevator recognition achieving 75% sensitivity and 97% specificity, and floor change detection surpassing 95% sensitivity and nearly 98% specificity (which translates to nearly 97% accuracy). This comprehensive solution, emphasizing the critical role of precise vertical positioning, signifies an advancement in tracking within urban structures.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Visible Light Positioning-Based Robot Localization and Navigation

Visible light positioning or VLP has been identified as a promising technique for accurate indoor localization utilizing pre-existing lighting infrastructure. Robot navigation is one of the many potential applications of VLP. Recent literature shows a small number of works on robots being controlled by fusing location information acquired via VLP that uses a rolling shutter effect camera as a receiver with other sensor data. This paper, in contrast, reports on the experimental performance of a cartesian robot that was controlled solely by a VLP system using a cheap photodiode-based receiver rigidly attached to the robot’s end-effector. The receiver’s position was computed using an inverse-Lambertian function for ranging followed by multi-lateration. We developed two novel methods to leverage the VLP as an online navigation system to control the robot. The position acquired from the VLP was used by the algorithms to determine the direction the robot needed to move. The developed algorithms guided the end-effector to move from a starting point to target/destination point(s) in a discrete manner, determined by a pre-determined step size. Our experiments consisted of the robot autonomously repeating straight line-, square- and butterfly-shaped paths multiple times. The results show median errors of 27.16 mm and 26.05 mm and 90 percentile errors of 37.04 mm and 47.48 mm, respectively, for the two methods.

Studies on indoor positioning algorithms using BLE Beacons

Global positioning systems have become very popular, helping locate people and objects. Currently, with greater specificity, indoor location services are gaining more relevance. Despite the recent developments, the technology for indoor localization is still incipient, demanding better accuracy, lower costs, and greater practicality. Therefore, this paper presents a study of a technique for indoor localization with Bluetooth low-energy beacons. In addition, we propose and perform experiments to implement a low-cost, low-power, BLE-enabled device-based indoor positioning system. With such devices, we calculate the distance between the emitters and the receiver using known techniques and determine the user’s location. The results we obtained reduced the time and complexity of calibrating the offline phase of the systems. However, the accuracy of the localization is related to the accuracy of the distance estimation.

5G Vs Wi-Fi Indoor Positioning: A Comparative Study

Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.

Confidentiality Preserved Federated Learning for Indoor Localization Using Wi-Fi Fingerprinting

For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an effective and privacy-protective federated architecture-based framework for location classification via Wi-Fi fingerprinting. The federated indoor localization classification (f-ILC) system that was suggested had distributed client–server architecture with data privacy for any and all related edge devices or clients. To try and evaluate the proposed f-ILC framework, different data from different sources on the Internet were collected and given in a format that had already been processed. Experiments were conducted with standard learning, federated learning with a single client, and federated learning with several clients to make sure that federated deep learning models worked correctly. The success of the f-ILC framework was computed using a number of factors, such as validation of accuracy and loss. The results showed that the suggested f-ILC framework performed better than traditional distributed deep learning-based classifiers in terms of accuracy and loss while keeping data secure. Due to its innovative design and superior performance over existing classifier tools, edge devices’ data privacy makes this proposed architecture the ideal solution.

JoLo: Multi-Device Joint Localization Based on Wireless Data Fusion

Many indoor localization techniques have been widely investigated. As the proportion of people with multiple mobile devices is increasing, an interesting research challenge arising from this growth is how to integrate data from multiple devices carried by a mobile user for localizing her/him. This work designs a multi-device joint localization system, called JoLo, to realize the idea using a group of off-the-shelf mobile devices carried by a user. The proposed system collects wireless fingerprints at the predefined training locations in the environment. Then, the system fuses the real-time wireless measurements observed by the multiple devices of a user into a summary list for localization reference. Finally, three fusion-based positioning algorithms are proposed to determine the users location based on the fused summary list. The three proposed algorithms reduce mean distance errors in localization to less than one meter in a lab environment. One of the fusion-based algorithms, namely cluster-based DF algorithm, can even improve the performance by 50% on average compared to the existing single-device localization techniques.

VILL: Toward Efficient and Automatic Visual Landmark Labeling

Of all indoor localization techniques, vision-based localization emerges as a promising one, mainly due to the ubiquity of rich visual features. Visual landmarks, which present distinguishing textures, play a fundamental role in visual indoor localization. However, few researches focus on visual landmark labeling. Preliminary arts usually designate a surveyor to select and record visual landmarks, which is tedious and time-consuming. Furthermore, due to structural changes (e.g., renovation), the visual landmark database may be outdated, leading to degraded localization accuracy. To overcome these limitations, we propose VILL , a user-friendly, efficient, and accurate approach for visual landmark labeling. VILL asks a user to sweep the camera to take a video clip of his/her surroundings. In the construction stage, VILL identifies unlabeled visual landmarks from videos adaptively according to the graph-based visual correlation representation. Based on the spatial correlations with selected anchor landmarks, VILL estimates locations of unlabeled ones on the floorplan accurately. In the update stage, VILL formulates an alteration identification model based on the judgments from different users to identify altered landmarks accurately. Extensive experimental results in two different trial sites show that VILL reduces the site survey substantially (by at least 65.9%) and achieves comparable accuracy.

Real-time robust and precise kernel learning for indoor localization under the internet of things

More and more applications under Internet of Things have strong need for more dedicated localization techniques. As a wireless signal strength measurement standard, received signal strength indicator (RSSI) nowadays is widely utilized as a quantity to build advanced fingerprint indoor localization techniques. However, the mixed noise such as Gaussian noise together with the abrupt noise always causes the deviation of the RSSI value and the mismatched fingerprints in the fingerprint-based method, which results in the deterioration of positioning accuracy. In this paper, we propose an online risk-sensitive localization technique named compositional online kernel indoor localization (COKIL), which further improves the performance and reduces the prediction variance under multi-path effects. Meanwhile, the Student’s t kernel is firstly employed in COKIL to fight against RSSI instability, which leads to the great performance improvement compared with the Gaussian kernel. Moreover, surprise criterion, novelty criterion and kernel orthogonal matching pursuit are embedded into COKIL to reduce the size of the neural networks. Comparing their performances in experiments, surprise criterion is the optimal sparse method in practice. Finally, a new model-based technique, RSSIq, is proposed to deal with the missing fingerprints, which significantly improves the performance in indoor environment compared to traditional path-loss model.

Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints.

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

Enhanced Indoor Localization Technique Based on Point Cloud Conversion Image Matching

Group teaching optimization with improved Chan-Taylor algorithm for 3D indoor localization

In recent years, indoor localization has become a hot research topic and several researchers have paid attention to design accurate 3-Dimensional (3D) indoor localization techniques. Existing localization techniques cannot be sufficient for 3D indoor localization due to the existence of interferences and the need for high precision. The most widely used option for outdoor localization is known as the global positioning system (GPS), and its precision in location is often within a 10-meter margin of error. Problems arise in the "final mile" of the localization field due to the complicated impediments that buildings provide, which encourages a momentum of indoor localization. Traditional methods of indoor localization are either dependent on range or fingerprinting, which means that the predeployment process takes a significant amount of time and work to complete. The non-line of sight propagation due to the building shielding and background interferences results in high error on the application of Ultra-Wideband technologies to indoor localization. For resolving this issue, this paper presents a novel group teaching optimization algorithm with improved Chan-Taylor algorithm (GTOA-ICTA) for 3D indoor localization. The GTO-ICTA technique initially employs the GTOA for transforming the target problem into the target of variations among the determined and original positions. Besides, the ICTA technique is employed for computing the accurate positions of the targets. The integration of GTOA and ICTA techniques helps to properly achieve maximum localization performance. For validating the enhanced performance of the presented GTOA-ICTA technique, a set of simulations were performed and the results are examined interms of different aspects. A comprehensive results analysis of the proposed GTOA-ICT technique takes place in positioning error with the minimum error of 0.1245, and the proposed method has reached increased results with the maximum error of 0.9660. Moreover the GTOA-ICT has obtained minimum average error of 0.6432, least Standard deviation of 0.4387. The resultant experimental outcomes highlighted the enhanced performance of the GTOA-ICTA techniques over the recent state of art techniques interms of different measures.

A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

In this research work an effective indoor localization mechanism has been implemented for smart devices, advancements of mobile-Internet and multimedia applications. Indoor localization is one of recent technology, it is a location-based service (LBS), this work has been facilitated to commercial and civilian industries. The LSB can useful in many tools such as public security, disaster management, and positioning navigation. Several research works have been concentrated on design of accurate 2D indoor localization techniques. Since 3D indoor localization techniques offer numerous benefits compared to 2D model. In this investigation a Novel 3D Indoor Node Localization Technique has been proposed using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO- WLSE algorithm aims to develop the localization accuracy with reduced computational time. The OBSO algorithm is employed for approximating initial locations of the targets, these results can minimize NLOS error. The precise location of target has been identified through WLSE technique as well as OBSO can predict initial location. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. The designed model prototype simulation has been run on MATLAB software with NS3 Tool Box. The measures like accuracy 98.45%, sensitivity 96.34%, recall 94.67%, 3D indoor localization detection rate 19.25% improvement and throughput 97.34% have been attained. The localization error, range error and transmission range performance measures are used for experimental evolution. The results recommended that proposed model is robust for navigation associated apps.

GC-Loc

Indoor localization techniques play a fundamental role in empowering plenty of indoor location-based services (LBS) and exhibit great social and commercial values. The widespread fingerprint-based indoor localization methods usually suffer from the low feature discriminability with discrete signal fingerprint or high time overhead for continuous signal fingerprint collection. To address this, we introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc. We have conducted extensive experiments in three different trial sites, and the experimental results demonstrate the superiority of GC-Loc, outperforming the comparison schemes by a wide margin (reducing the mean localization error by more than 42%).

Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems

In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor.

Wireless communication indoor positioning method in 5G sub-station using deep neural network and location fingerprint algorithm

The use of outside positioning systems based on Global Navigation Satellite System (GNSS) for inside location is impractical due to a number of drawbacks which makes the use of machine learning for its solution. Location fingerprinting has historically relied on shallow learning methods. This research proposes novel technique in indoor positioning technique for 5 G substation by deep learning techniques to analyse the indoor positioning using deep neural networks using 5 G NR signal based weighted cross correlation kernel support vector machine. The experimental results shows the comparative analysis between proposed as well as existing technique in terms of SNR, positioning accuracy, positioning error, average relative error, spectrum efficiency which attained SINR of 88 %, positioning accuracy of 96 %, Positioning error of 65 %, average relative error of 56 %, spectral efficiency of 98 % and computational complexity of 51 %. It requires one base station and works with the majority of wireless networks. Its application potential is greater and its computational complexity is less than other indoor localization techniques now in use.