Smartphone-based Indoor Localization Research Articles

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Smartphone-Based Indoor Localization Using Machine Learning and Multisource Information Fusion

Smartphone-Based Indoor Localization Using Machine Learning and Multisource Information Fusion

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.

Smartphone-Based Indoor Localization via Network Learning With Fusion of FTM/RSSI Measurements

This letter proposes a deep neural network (DNN)-based indoor localization approach that leverages WiFi Fine Timing Measurement (FTM) and Received Signal Strength Indicator (RSSI) as environment features to provide accurate location estimation. Our method uses DNN with raw FTM and RSSI measurements for self-learning and produces enhanced ranging information in the presence of measurement noise. Experimental data was obtained from real-world settings using commercial off-the-shelf devices in two different indoor office environments. The proposed solution was evaluated regarding the localization Mean Squared Error, demonstrating remarkable accuracy and outperforming state-of-the-art methods.

Practical and Accurate Indoor Localization System Using Deep Learning.

Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.

Indoor Localization Fusing WiFi With Smartphone Inertial Sensors Using LSTM Networks

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window-based displacement scheme is designed to generate a time-series-based feature data set. We further apply the long short-term memory (LSTM) network for data fusion and localization on this data set by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

Smartphone-based indoor localization techniques: State-of-the-art and classification

Smartphone is now undeniably essential and a very functional tool for everyone. As the need for more powerful computations, faster communications, and more sophisticated location-based services, the number of powerful embedded sensors also becomes indispensable in smartphones. Leveraging the huge collection of raw data coming from these commodity sensors gives birth to a myriad of mobile applications that are mostly centered on the users’ location. This survey explores the sensing capabilities of different smartphone sensors and how they become more functional in the context of wireless-based localization. It identifies the different techniques toward efficient indoor localization using the modalities inherent in smartphones. A taxonomy based on the notable approaches used in the system such as mapping, path loss prediction, and dead reckoning is also defined. The strengths and weaknesses of each system and identify and compare the technologies used are highlighted. Finally, open issues and challenges for future works are presented.

Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration

Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration

Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter

In recent days, accurate localization becomes essential for enabling smartphone-based navigation to attain maximum accuracy in the construction of the real world.Fingerprint-based localization is the widespread solution to achieve and assure effective performance. In this study, a new fingerprint-based localization model using a bat algorithm (BA) is presented stimulated by the echolocation nature of microbats. The presented model adapts BA for estimating the location information. Initially, the presented model applies a Bayesian-rule based objective function. Then, the BA is used for improving the accuracy and analyzing the effects of the initial position of the bats on the localization outcome. For mitigating the estimation error, the Kalman filter is employed for updating the initially determined position using the BA for tracking purposes. The experimental analysis indicated an improvement in real-time performance and decrease in computation complexity. The presented model also obtained maximum localization accuracy with minimum localization error over the compared methods.

Smartphone based indoor localization using permanent magnets and artificial intelligence for pattern recognition

Smartphone-based indoor localization methods are frequently employed for position estimation of users inside enclosures like malls, conferences, and crowded venues. Existing solutions extensively use wireless technologies, like Wi-Fi, RFID, and magnetic sensing. However, these approaches depend on the presence of active beacons and suitable mapping surveys of the deployed areas, which render them highly sensitive to the local ambient field clutters. Thus, current localization systems often underperform. We embed small-volume and large-moment magnets in pre-known locations and arrange them in specific geometric forms. Each constellation of magnets creates a super-structure pattern of supervised magnetic signatures. These signatures constitute an unambiguous magnetic environment with respect to the moving sensor carrier. The localization algorithm learns the unique patterns of the scattered magnets during training and detects them from the ongoing streaming of data during localization. Our work innovates regarding two essential features: first, instead of relying on active magnetic transmitters, we deploy passive permanent magnets that do not require a power supply. Second, we perform localization based on smartphone motion rather than on static positioning of the magnetometer. Therefore, we present a novel and unique dynamic indoor localization method combined with artificial intelligence (AI) techniques for post-processing. Experimental results have demonstrated localization accuracy of 95% with a resolution of less than 1m.

A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.

Improved Smartphone-Based Indoor Localization System Using Lightweight Fingerprinting and Inertial Sensors

The existing radio frequency-based positioning approaches widely used for indoor localization-based service (LBS) are fingerprinting and trilateration and their integration with the inertial sensors-based dead reckoning system. However, these localization methods have practical limits and challenges due to unstable signal strength, the cost of offline workload, computational complexity, terminal device heterogeneity, and accumulated sensor error. We propose a smartphone-based indoor localization system using weighted Spearman’s foot-rule (WSF)-based probabilistic fingerprinting for reliable smartphone localization service. This localization system adopts a real-time fingerprinting position error estimation approach realizing an adaptive extended Kalman filter (AEKF) to integrate the proposed fingerprinting localization with inertial measurement unit (IMU)-based localization. This proposed WSF-based smartphone localization uses a Gaussian process regression (GPR)-based signal prediction module to deal with fingerprinting localization’s offline workload. Furthermore, the smartphone localization system’s expected high computational complexity is controlled by employing a data-clustering module. The proposed WSF also employs a rank vector that helps mitigate the effect of terminal device heterogeneity. The proposed localization system is experimentally evaluated at two different representative indoor environments. Experimental results obtained by real field deployment show that the mean error is 2.06 m in an elongated hallway corridor and 3.47 m in the crowded and well-furnished wide area.

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Novel weighted ensemble classifier for smartphone based indoor localization

Indoor localization systems have the capability to change the way of providing location-based services in a closed environment. Though there is no agreed-upon technology that works best in indoor, WiFi signal is an important alternative as most of such places are covered by WiFi Access Points (APs). In this paper, the problem of indoor localization is investigated from the perspective of expert systems through applying machine learning techniques. The significant variation of WiFi signal strength with ambient conditions as well as device configuration badly affects the localization accuracy. Thus, the fingerprinting effort required to train a localization system subject to context heterogeneity is huge. The uncertainty in localization performance due to varying contexts is hardly investigated in the literature. Consequently, the main contribution of this paper is to propose a weighted ensemble classifier based on Dempster–Shafer belief theory to efficiently handle context heterogeneity. Here, the context is defined in terms of different smartphone configurations used for training and testing the system as well as temporal variation of signals. The method presented here utilizes the Dempster–Shafer theory of belief functions to calculate the weights of the base learners in the decision of the ensemble. Belief theory is applied here to handle the inherent uncertainty in WiFi signal variations due to heterogeneous context. Real life experiments are conducted for two datasets, JUIndoorLoc and UJIIndoorLoc at different granularity levels. For JUIndoorLoc, with state-of-the-art classifiers, 86–97% accuracy can be achieved for 10-fold cross-validation. However, when the training context differs from the test conditions, accuracy drops to 62–87%. In such a scenario, the proposed weighted ensemble technique is found to achieve almost 98% localization accuracy when RSSIs, mean and variance of RSSIs are considered as features. The technique can lead to an effective expert system for indoor localization at varying granularity levels. Such systems would be beneficial for pervasive indoor positioning applications as no dedicated infrastructure is needed for positioning.

WiFi and Vision-Integrated Fingerprint for Smartphone-Based Self-Localization in Public Indoor Scenes

Smartphone-based indoor localization systems are increasingly needed in various types of applications. This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m2 and a mega-mall of 7200 m2 with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. The final localization errors after metric localization are less than a half meter on average.

Smartphone-Based Indoor Localization With Integrated Fingerprint Signal

Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

An Experimental Heuristic Approach to Multi-Pose Pedestrian Dead Reckoning Without Using Magnetometers for Indoor Localization

Pedestrian dead reckoning (PDR) is a potential technique for smartphone-based indoor localization using the built-in accelerometers and gyroscopes. However, most current PDR positioning approaches restrict the pose to carry a phone, such as holding it in front of the body during tracking. In addition, heading determination is the primary error sources in PDR systems, and hence, a magnetometer is normally required to reduce heading errors. However, in indoor environments, magnetometer outputs would be seriously distorted by magnetic disturbances from the building structures and electronics. In this paper, an experimental heuristic approach to multi-pose PDR (mpPDR) indoor positioning for structured environments has been proposed. Six poses and four modes to carry a phone could be recognized, and then, on the basis of different modes, an experimental approach to the heading determination without using magnetometers has been presented to reduce the heading error considering the effects of turning and hand-shaking events. The experimental results indicate that the proposed approach supports four smartphone modes with a high position accuracy of 97.99%.

JUIndoorLoc: A Ubiquitous Framework for Smartphone-Based Indoor Localization Subject to Context and Device Heterogeneity

A new era of ubiquitous indoor location awareness is on the horizon especially for context sensing, ambient assisted living and many other smart city applications. Although indoor localization plays a pivotal role in making the environment smarter, it is still very difficult to compare state-of-the-art localization algorithms due to the scarcity of standard databases. Publicly available databases are neither fine-grained nor contain data for different conditions. Received Signal Strength Indicator (RSSI) of Wi-Fi signals vary with indoor environment (open/closed room, presence/absence of user, temperature etc.) and scanning smart hand-held devices. Thus, localization accuracy varies with various environmental conditions and also granularity of location points (cell). Consequently, in this paper, our contribution is two-fold. First, we present a comprehensive indoor localization dataset, subject to different domains-spatial, temporal, context and device. RSSI data has been collected with cell sizes as small as $$1\,{\mathrm{m}}\times 1\,{\mathrm{m}}$$ from three floors of a building of our University using an Android application built for this purpose. This multi-floor dataset is available online at https://drive.google.com/open?id=1_z1qhoRIcpineP9AHkfVGCfB2Fd_e-fD. Our experimental results show that maximum of $$71.78\%$$ classification accuracy can be achieved for state-of-the-art classifiers when training and testing samples are taken in different environmental conditions and from smartphones having different configurations. Single classifier cannot easily be modified to suit these variations without loosing its generality. So, to overcome these conditional dependencies, our second contribution is to propose a framework for indoor localization, JUIndoorLoc and design an ensemble of condition specific classifiers as part of the framework to take care of context and device heterogeneity. Consequently, this ensemble of condition specific classifiers is implemented and found to predict a location with $$91.74\%$$ accuracy (1.87 m) for our dataset.