Indoor Localization Problem Research Articles

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.

Received Signal Strength Indicator-Based Indoor Localization Using Distributed Set-Membership Filtering.

Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.

An Indoor Localization Algorithm Based on Modified Joint Probabilistic Data Association for Wireless Sensor Network

Due to the localization accuracy of global positioning system (GPS) cannot meet the requirements in indoor environment, the wireless sensor network (WSN) techniques are efficient methods to cope with this problem. The WSN-based indoor localization techniques have become effective methods to solve the problem of indoor localization. Since the nonline-of-sight (NLOS) effect could severely induce the localization accuracy, the primary challenge in indoor localization is the handling of NLOS errors. Due to multipath effect, near-far effect, obstacle occlusion, etc., the NLOS errors become very sophisticated. Aiming at this problem, a modified joint probabilistic data association localization (MJPDA) algorithm is proposed in this article. First, MJPDA obtains a series of preprocessing virtual points by grouping the measurements. Then, the measurements are divided into two categories, that is, line-of-sight (LOS) and NLOS, by virtual points’ density. In the case of LOS, extended Kalman filter (EKF) is used for processing. For the NLOS case, a series of particles are first generated around the prediction point, and then modified JPDA is used to data association of the virtual points and the particles. Simulations results illustrate that MJPDA is superior to MPDA algorithm and the traditional EKF algorithm in localization accuracy and robustness. Finally, we perform the real experiment to verify the performance of MJPDA. The experimental results demonstrate that MJPDA has prominent performance in mitigating large NLOS errors.

Received Signal Strength-Based Indoor Localization Using Hierarchical Classification.

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

Location Fixing and Fingerprint Matching Fingerprint Map Construction for Indoor Localization

Building the fingerprint map for indoor localization problems is a labour-intensive and time-consuming process. However, due to its direct influence on the location estimation accuracy, finding a proper mechanism to construct the fingerprint map is essential to enhance the position estimation accuracy. Therefore, in this work, we present a fingerprint map construction technique based on location fix determination and fingerprint matching motivated by the availability of advanced sensing capabilities in smartphones to reduce the time and labour cost required for the site survey. The proposed Location Fixing and Finger Matching (LFFM) method use a landmark graph-based localization approach to automatically estimate the location fixes for the Reference Points and matching the collected fingerprints, without requiring active user participation. Experimental results show that the proposed LFFM is faster than the manual fingerprint map construction method and remarkably improves the positioning accuracy.

Vivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing

Indoor localization and navigation have a great potential of application, especially in large indoor spaces where people tend to get lost. The indoor localization problem is the fundamental of an indoor navigation system. Existing research and commercial efforts have leveraged wireless-based approaches to locate users in indoor environments. However, the predominant wireless-based approaches, such as WiFi and Bluetooth, are still not satisfactory, either not supporting commodity devices, or being vulnerable to environmental changes. These issues make them hard to deploy and maintain. In this paper, we present Vivid, a mobile device-friendly indoor localization and navigation system that leverages visual cues as the cornerstone of localization. By leveraging the computation power at the extreme internet edges, Vivid to a large extent overcomes the difficulties brought by resource-intensive image processing tasks. We propose a grid-based algorithm that transforms the feature map into a grid, with which finding the path between two positions can be easily obtained. We also leverage deep learning techniques to assist in automatic map maintenance to adapt to the visual changes and make the system more robust. With edge computing, user privacy is preserved since the visual data is mainly processed locally and detected dynamic objects are removed immediately without saving to databases. The evaluation results show that: i) our system easily outperforms the existing solutions on COTS devices in localization accuracy, yielding decimeter-level error; ii) our choice of the system architecture is scalable and optimal among the available ones; iii) the automatic map maintenance mechanism effectively ameliorates the localization robustness of the system.

Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

Mobile devices are a common target for augmented reality applications, especially for showing contextual information in buildings or construction sites. A prerequisite of contextual information display is the localization of objects and the device in the real world. In this paper, we present our approach to the problem of mobile indoor localization with a given building model. The approach does not use external sensors or input. Accurate external sensors such as stationary cameras may be expensive and difficult to set up and maintain. Relying on already existing external sources may also prove to be difficult, as especially inside buildings, Internet connections can be unreliable and GPS signals can be inaccurate. Therefore, we try to find a localization solution for augmented reality devices that can accurately localize itself only with data from internal sensors and preexisting information about the building. If a building has an accurate model of its geometry, we can use modern spatial mapping techniques and point-cloud matching to find a mapping between local device coordinates and global model coordinates. We use normal analysis and 2D template matching on an inverse distance map to determine this mapping. The proposed algorithm is designed to have a high speed and efficiency, as mobile devices are constrained by hardware limitations. We show an implementation of the algorithm on the Microsoft HoloLens, test the localization accuracy, and offer use cases for the technology.

Deep CNN for Indoor Localization in IoT-Sensor Systems.

Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.

RSS-Based Indoor Localization Using Belief Function Theory

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners —This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.

Indoor Region Localization With Asynchronous Sensing Data: A Bayesian Probabilistic Model

Obtaining accurate location information of tracked objects is the cornerstone of providing high-quality location-based services (LBSs). Recently, passive localization has become an increasingly important research theme, attracting the attention from both academic and industrial communities. However, existing Wi-Fi fingerprinting-based passive localization methods mainly focus on the indoor localization problem and require Wi-Fi detection equipment to scan wireless communications simultaneously. In this paper, we propose the concept of indoor region localization, which aims at estimating the region where an object stayed for a given time interval. Moreover, a Bayesian probabilistic model is presented to cope with this issue. The key novelty of our method lies in that it allows Wi-Fi detection equipment to behave in an asynchronous fashion, which eliminates synchronization concerns when implementing the positioning system. Specifically, it first introduces a time window for each timestamp, and then aggregates all the sensing records in the given time window to obtain an RSSI measurement vector. Afterward, a kernel function is utilized to measure the conditional probability with respect to every fingerprint, and all these probabilities are combined to obtain the posterior probabilities corresponding to different regions. Extensive experiments are conducted on both public and proprietary data sets and our method significantly outperforms all the comparing algorithms in terms of each evaluation metric.

Simultaneous Localization and Mapping Using the Phase of Passive UHF-RFID Signals

An indoor simultaneous localization and mapping (SLAM) problem for a mobile robot using the Radio Frequency IDentification (RFID) technology is considered. The system consists of a reader, installed on the robot, which measures the phase shift of the UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. The position of the tags is unknown and only an approximate knowledge of the ceiling height is available at the beginning. A Rao-Blackwellized Particle Filtering approach is proposed to fuse the RFID measurements with the robot odometry and to determine both the robot pose and the tag coordinates. Simulative and experimental results are reported to illustrate the effectiveness of the proposed approach: the precision obtained is in the order of a few cm.

Ensemble learning particle swarm optimization for real-time UWB indoor localization

This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.

FSELM: fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints

Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and accurate positioning system has been found. In most traditional approaches, solving the indoor localization problem requires the availability of a large number of labeled training samples, the collection of which requires considerable manual effort. Previous research has not provided a means of simultaneously reducing human calibration effort and improving location accuracy. This paper introduces fusion semi-supervised extreme learning machine (FSELM), a novel semi-supervised learning algorithm based on the fusion of information from Wi-Fi and Bluetooth Low Energy (BLE) signals. Unlike previous semi-supervised methods, which consider multiple signals individually, FSELM fuses multiple signals into a unified model. When applied to sparsely calibrated localization problems, our proposed method is advantageous in three respects. First, it can dramatically reduce the human calibration effort required when using a semi-supervised learning framework. Second, it utilizes fused Wi-Fi and BLE fingerprints to markedly improve the location accuracy. Third, it inherits the beneficial properties of ELMs with regard to training and testing speeds because the input weights and biases of hidden nodes can be generated randomly. As demonstrated by experimental results obtained on practical indoor localization datasets, FSELM possesses a better semi-supervised manifold learning ability and achieves higher location accuracy than several previous batch supervised learning approaches (ELM, BP and SVM) and semi-supervised learning approaches (SELM, S-RVFL and FS-RVFL). Moreover, FSELM needs less training and testing time, making it easier to apply in practice. We conclude through experiments that FSELM yields good results when applied to a multi-signal-based semi-supervised learning problem. The contributions of this paper can be summarized as follows: First, the findings indicate that effective multi-data fusion can be achieved not only through data-layer fusion, feature-layer fusion and decision-layer fusion but also through the fusion of constraints within a model. Second, for semi-supervised learning problems, it is necessary to combine the advantages of different types of data by optimizing the model’s parameters.

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

Impact localization in dispersive waveguides based on energy-attenuation of waves with the traveled distance

Abstract An algorithm is introduced to solve the general multilateration (source localization) problem in a dispersive waveguide. The algorithm is designed with the intention of localizing impact forces in a dispersive floor, and can potentially be used to localize and track occupants in a building using vibration sensors connected to the lower surface of the walking floor. The lower the wave frequencies generated by the impact force, the more accurate the localization is expected to be. An impact force acting on a floor, generates a seismic wave that gets distorted as it travels away from the source. This distortion is noticeable even over relatively short traveled distances, and is mainly caused by the dispersion phenomenon among other reasons, therefore using conventional localization/multilateration methods will produce localization error values that are highly variable and occasionally large. The proposed localization approach is based on the fact that the wave’s energy, calculated over some time window, decays exponentially as the wave travels away from the source. Although localization methods that assume exponential decay exist in the literature (in the field of wireless communications), these methods have only been considered for wave propagation in non-dispersive media, in addition to the limiting assumption required by these methods that the source must not coincide with a sensor location. As a result, these methods cannot be applied to the indoor localization problem in their current form. We show how our proposed method is different from the other methods, and that it overcomes the source-sensor location coincidence limitation. Theoretical analysis and experimental data will be used to motivate and justify the pursuit of the proposed approach for localization in a dispersive medium. Additionally, hammer impacts on an instrumented floor section inside an operational building, as well as finite element model simulations, are used to evaluate the performance of the algorithm. It is shown that the algorithm produces promising results providing a foundation for further future development and optimization.

Indoor localization using augmented ultra-high-frequency radio frequency identification system for Internet of Things

This article addresses the problem of indoor localization with an augmented ultra-high-frequency radio frequency identification system. In the infrastructure of augmented ultra-high-frequency radio...

Impact of IEEE 802.15.4 Communication Settings on Performance in Asynchronous Two Way UWB Ranging

The ultra wideband (UWB) radio signals are known for their good time resolution enabling implementation of accurate localization and tracking. The recent appearance of commercial UWB transceivers in masses on the market has boosted the interest towards this technology and facilitated its use not just for research, but also for business. In this paper we focus on the problem of UWB-based wireless indoor localization of machines and humans by means of IEEE 802.15.4-2015 high rate pulse repetition UWB technology and specifically the accuracy of such localization. Namely, we report the results of an extensive experimental study revealing the effect of various communication settings on the accuracy of indoor localization for the proposed earlier asymmetric localization protocol. The conducted experiments lasted over 200 hours almost nonstop and involved transmission of more than 30 million ranging packets. In the experiments we have tested over 200 different modes and explored the effect of seven different parameters on the UWB ranging performance. The presented results reveal that the communication settings need to be accounted for when determining the time of flight using UWB. Also we show that the accuracy of ranging is strongly affected by the used channel, data rate and pulse repetition frequency. Finally, we note that the increase of the UWB transceiver’s temperature due to self-heating has a strong effect on the results of the localization.

An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones.

With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user’s motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances.

A Low-Cost Indoor Localization System for Energy Sustainability in Smart Buildings

This paper proposes a low-cost indoor localization system in the context of smart buildings in which customized services are provided to occupants, considering energy consumption issues. To offer indoor services, it is necessary to consider information concerning to the identity and location of the occupants, as well as the accuracy required in the location data to provide these services in a comfortable and energy efficient way. We propose a mechanism for solving these localization requirements, using radio frequency identification and infrared data. The computational techniques implemented in this paper in order to solve the indoor localization problem are: 1) a radial basis function (RBF) network, which is in charge of estimating the user position and 2) a particle filter, which is in charge of estimating the next position of users based on the previous positions estimated by the RBF. This localization mechanism is tested in a reference smart building that houses an automation system for collecting data and controlling the devices and appliances of the building. The results obtained from the assessments performed are very satisfactory in terms of accuracy and precision of the user location data, providing a low-cost solution for ambient adaptation based on human presence.

A swarm-based approach to real-time 3D indoor localization: Experimental performance analysis

In this paper, the problem of indoor localization in wireless networks is addressed relying on a swarm-based approach. We assume to know the positions of a few number of sensor nodes, denoted as anchor nodes (ANs), and we aim at finding the position of a target node (TN) on the basis of the estimated distances between each AN and the considered TN. Since ultra wide band (UWB) technology is particularly suited for localization purposes (owing to its remarkable time resolution), we consider a network composed of UWB devices. More precisely, we carry out an experimental investigation using the PulsOn 410 ranging and communication modules (RCMs) produced by time domain. Using four of them as ANs and one of them as TN, various topologies are considered in order to evaluate the accuracy of the proposed swarm-based localization approach, which relies on the pairwise (AN-TN) distances estimated by the RCMs. Then, we investigate how the accuracy of the proposed localization algorithm changes if we apply to the distance estimates a recently proposed stochastic correction, which is designed to reduce the distance estimation error. Our experimental results show that a good accuracy is obtained in all the considered scenarios, especially when applying the proposed swarm-based localization algorithm to the stochastically corrected distances. The obtained results are satisfying also in terms of software execution time, making the proposed approach applicable to real-time dynamic localization problems.