Indoor Localization Scheme Research Articles

Comparison of Indoor Localization Scheme for Multistory Environment

Outdoor localization nowadays is a part of our daily life. However, such useful localization method as the Global Positioning System (GPS) fails inside buildings. Popular alternative is to utilize the Indoor Positioning System (IPS) technologies that are accessible indoors. There is a wide range of IPS technologies that can be used within an indoor environment. Moreover, more attention has been given to IPS for multistory buildings recently. In indoor localization, different techniques and methods are used for distance and position estimation. This paper will focus on indoor localization technologies and the comparison of indoor localization methods focusing on multistory environment which will improve the localization accuracy through various models and techniques.

Real-Time Indoor Localization for Smartphones Using Tensor-Generative Adversarial Nets.

High-accuracy location awareness in indoor environments is fundamentally important for mobile computing and mobile social networks. However, accurate radio frequency (RF) fingerprint-based localization is challenging due to real-time response requirements, limited RF fingerprint samples, and limited device storage. In this article, we propose a tensor generative adversarial net (Tensor-GAN) scheme for real-time indoor localization, which achieves improvements in terms of localization accuracy and storage consumption. First, with verification on real-world fingerprint data set, we model RF fingerprints as a 3-D low-tubal-rank tensor to effectively capture the multidimensional latent structures. Second, we propose a novel Tensor-GAN that is a three-player game among a regressor, a generator, and a discriminator. We design a tensor completion algorithm for the tubal-sampling pattern as the generator that produces new RF fingerprints as training samples, and the regressor estimates locations for RF fingerprints. Finally, on real-world fingerprint data set, we show that the proposed Tensor-GAN scheme improves localization accuracy from 0.42 m (state-of-the-art methods kNN, DeepFi, and AutoEncoder) to 0.19 m for 80% of 1639 random testing points. Moreover, we implement a prototype Tensor-GAN that is downloaded as an Android smartphone App, which has a relatively small memory footprint, i.e., 57 KB.

Improving performance of indoor localization using compressive sensing and normal hedge algorithm

Accurate indoor localization technologies are currently in high demand in wireless sensor networks, which strongly drive the development of various wireless applications including healthcare monitoring, patient tracking and endoscopic capsule localization. The precise position determination requires exact estimation of the time varying characteristics of wireless channels. In this paper, we address this issue and propose a three-phased scheme, which employs an optimal single stage TDOA/FDOA/AOA indoor localization based on spatial sparsity. The first contribution is to formulate the received unknown signals from the emitter as a compressive sensing problem. Then, we solve an $\ell_1$ minimization problem to localize the emitter's position. To combat the nonstationary behavior of wireless channels between sensor nodes, the results of our proposed localization algorithm are finally fused using a novel fusion method based on the adaptive normal hedge algorithm. To improve the accuracy of the estimated location, an optimal set of weighed coefficients are derived through introducing a new loss function. Monte Carlo simulation results show that the accuracy of the proposed localization framework is superior compared to the existing indoor localization schemes in low SNR regimes.

Indoor static localization based on Fresnel zones model using COTS Wi-Fi

Compared with outdoor localization using Global Positioning System (GPS), indoor localization is more complex and difficult due to the shadowing, fading and reflecting of radio. Thus, pioneers struggled for accurate device-free indoor localization and many creative schemes were proposed. However, these schemes still have defects and deficiencies. Mode-based schemes usually extract features of radio signal as fingerprints to match the target positions and signal modes. The training of features is time-consuming and labor-intensive because the fingerprints need to update every time when indoor environment changes. Model-based schemes universally establish empirical model to locate target, but the models are mostly un-robust in multiple experimental scenes. In this paper, we attempt to localize target in indoor environment based on Fresnel zones model of radio communication that could determine which elliptic region the target located in according to the phase of Channel State Information (CSI) obtained from Commercial Off-The-Shelf (COTS) Wi-Fi device. In addition, we focus on the interference on radio signal in indoor environment and propose specific solutions to remove phase offset and measurement error caused by insufficient hardware or background fluctuation. Therefore, our proposed system is more accurate compared with existing works. Results show the average error of our scheme is 33.6 cm in 47.6 m2 meeting room and 31.2 cm in 40.7 m2 office room while state-of-the-art work (MFDL) is 75 cm in 72 m2.

High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning

Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demand for service-based indoor positioning. Meanwhile, high accuracy localization is very important for mobile robots in various scenes including industrial, domestic and public transportation workspace. In this paper, an indoor robot VLP localization system based on Robot Operating System (ROS) is presented for the first time, aiming at promoting the application of VLP in mature robotic system. On the basis of our previous researches, we innovatively designed a VLP localization package which contains the basic operation control of the robots, the features extraction and recognition of the LED-ID, cm-level positioning, and robust dynamic tracking algorithms. This package exploited the proposed lightweight algorithm, distributed framework design, the loose coupling characteristics of the ROS, and the message communication methods among different nodes. What's more, an efficient LED-ID detection scheme is proposed to ensure the lightweight and accuracy of the positioning. A prototype system has been implemented on a Turtlebot3 Robot 1 1 Experiment Demonstration is available at: https://kwanwaipang.github.io/Image/ROS.mp4 . . Experimental results show that the proposed system can provide robot indoor positioning accuracy within 1 cm and an average computational time of only 0.08 s.

Large-Scale Deep Learning Framework on FPGA for Fingerprint-Based Indoor Localization

Localization Based Service (LBS) has become as one of the most important applications in modern daily life. Positioning technologies for outdoor environments are relatively mature because of the wide coverage of satellite navigating systems such as the Global Positioning System (GPS). In contrast, indoor localization remains a great challenge due to the fluctuated radio propagation environment. In addition to the high requirement of accuracy, an indoor localization system should also be low cost, low power consumption, and ubiquitous availability in mobile devices. To this end, fingerprint-based indoor localization schemes have served as an effective methodology to satisfy those requirements and have attracted more and more research attentions. In this paper, we present a scalable Deep Neural Network (DNN) architecture with Denoising Auto-encoder for Fingerprint-based Indoor Localization (called “SDNNLoc”) based-on FPGA implementation. First, a scalable stacked denoising auto-encoder is introduced to extract features from the fingerprint database for robustness and accuracy. Then, a generic parameterized DNN accelerator generating & optimization framework is proposed for FPGA implementation. In addition, we also demonstrate a WiFi-based fingerprinting indoor localization system for a crowdsensed university campus scenario. The experimental results show that the proposed DNN framework and its FPGA implementation are feasible for efficient and accurate indoor localization with good performance and high scalability.

TOA-Based Indoor Localization and Tracking With Inaccurate Floor Plan Map via MRMSC-PHD Filter

This paper proposes a novel indoor localization scheme to jointly track a mobile device (MD) and update an inaccurate floor plan map using the time-of-arrival measured at multiple reference devices (RDs). By modeling the floor plan map as a collection of map features, the map and MD position can be jointly estimated via a multi-RD single-cluster probability hypothesis density (MSC-PHD) filter. Conventional MSC-PHD filters assume that each map feature generates at most one measurement for each RD. If single reflections of the detected signal are considered as measurements generated by map features, then higher-order reflections, which also carry information on the MD and map features, must be treated as clutter. The proposed scheme incorporates multiple reflections by treating them as virtual single reflections reflected from inaccurate map features and traces them to the corresponding virtual RDs (VRDs), referred to as a multi-reflection-incorporating MSC-PHD (MRMSC-PHD) filter. The complexity of using multiple reflection paths arises from the inaccuracy of the VRD location due to inaccuracy in the map features. Numerical results show that these multiple reflection paths can be modeled statistically as a Gaussian distribution. A computationally tractable implementation combining a new greedy partitioning scheme and a particle-Gaussian mixture filter is presented. A novel mapping error metric is then proposed to evaluate the estimated map's accuracy for plane surfaces. Simulation and experimental results show that our proposed MRMSC-PHD filter outperforms the existing MSC-PHD filters by up to 95% in terms of average localization and by up to 90% in terms of mapping accuracy.

Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

Hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning.

In this paper, we propose a hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning. The architecture and algorithm of the proposed positioning scheme are analyzed. We build and demonstrate two different positioning prototypes with single/multiple cells, which are based on a commercial light-emitting diode lamp and off-the-shelf mobile phone equipped with an image sensor and inertial measurement unit. Experimental results show that the proposed visible light positioning scheme can achieve both cell recognition and three-dimensional positioning with a single captured image of a single light source, and the positioning error is only several centimeters. Moreover, with the proposed hybrid positioning algorithm consisting of visible light positioning and pedestrian dead reckoning, we achieved real-time decimeter-level indoor location tracking continuously and robustly with sparse light-source beacons in an actual indoor scenario.

DP3: A Differential Privacy-Based Privacy-Preserving Indoor Localization Mechanism

Wi-Fi fingerprint-based indoor localization is regarded as one of the most promising techniques for location-based services. However, it faces serious problem of privacy disclosure of both clients’ location data and provider’s fingerprint database. To address this issue, this letter proposes a differential privacy (DP)-based privacy-preserving indoor localization scheme, called DP3, which is composed of four phases: access point (AP) fuzzification and location retrieval in client side and DP-based finger clustering and finger permutation in server side. Specifically, in AP fuzzification , instead of providing the measured full finger (including AP sequence and the corresponding received signal strength), a to-be-localized (TBL) client only uploads the AP sequence to the server. Then, the localization server utilizes the DP-enabled clustering to build the fingerprints related to the AP sequence into $k$ clusters, permutes these reference points in each cluster with exponential mechanism to mask the real positions of these fingerprints, and sends the modified data set to the TBL client. At client side, location retrieval phase estimates the location of the client. Theoretical and experimental results show that DP3 can simultaneously protect the location privacy of the TBL client and the data privacy of the localization server.

Indoor Localization Using <italic>K</italic>-Pairwise Light Emitting Diode Image-Sensor-Based Visible Light Positioning

A great deal of research has been devoted to studying indoor localization schemes for the development of location-based services. This study investigated an indoor localization system, including subsystems of nonline-of-sight (NLOS) image-sensor-based visible light communications (IS-VLCs) and the proposed $K$ -pairwise light-emitting diode (LED) image-sensor-based visible light positioning (IS-VLP), in a unified lighting environment. A smartphone received multiple LED identifiers through the designed NLOS IS-VLC and successfully performed the proposed $K$ -pairwise LED IS-VLP with $K = 3$ to achieve localization, with maximum localization errors of 2.92 and 3.54 cm being obtained for ceiling heights of 100 and 150 cm, respectively.

Indoor Smartphone Localization Based on LOS and NLOS Identification.

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization.

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

High-precision indoor three-dimensional localization scheme based on visible light communication using modified fruit fly optimization algorithm

Recently, indoor positioning system (IPS) based on visible light communication (VLC) has gradually become a hot research topic. However, many existing indoor localization schemes based on VLC only provide solutions of positioning on a two-dimensional plane. Some IPSs-based VLC that focus on three-dimensional (3-D) positioning usually contain various sensors or use complex algorithms. A high-precision indoor 3-D localization scheme based on VLC using modified fruit fly optimization algorithm (MFOA) is proposed. Since it is difficult to directly calculate the coordinate of PD receiver in 3-D positioning problem, we transform the 3-D positioning problem into a global optimization problem and design the corresponding fitness function according to the Lambertian radiation pattern, which can achieve a higher accuracy and reduce system complexity. By changing the structure of fly group in the traditional fruit fly optimization algorithm and designing an adaptive search scope, the MFOA is obtained to solve the positioning problem, which leads to a nice positioning effect. With a good convergence performance, our simulation shows an average error of 0.76 cm within 20 iterations in an indoor space of 4 m × 4 m × 6 m, and most 3-D positioning errors are <1.85 cm, which shows a good positioning performance. Therefore, our indoor localization scheme has a potential value in the actual practice of indoor localization.

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter.

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

Robot-assisted smartphone localization for human indoor tracking

Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment. Motivated by increasing research attention on location-aware human–robot interaction, we propose a robot-assisted human indoor localization scheme utilizing acoustic ranging between a self-localized mobile robot and smartphones. Data from the low-cost Kinect vision sensor are fused with smartphone-based acoustic ranging, and an extended Kalman filter based localization algorithm is developed for real-time dynamic position estimation and tracking. Real robot–smartphone experiments are performed, and performances are evaluated in various indoor environments under different environmental noises and with different human walking speed. Comparing to existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot.

Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi.

In this paper, we propose UILoc, an unsupervised indoor localization scheme that uses a combination of smartphone sensors, iBeacons and Wi-Fi fingerprints for reliable and accurate indoor localization with zero labor cost. Firstly, compared with the fingerprint-based method, the UILoc system can build a fingerprint database automatically without any site survey and the database will be applied in the fingerprint localization algorithm. Secondly, since the initial position is vital to the system, UILoc will provide the basic location estimation through the pedestrian dead reckoning (PDR) method. To provide accurate initial localization, this paper proposes an initial localization module, a weighted fusion algorithm combined with a k-nearest neighbors (KNN) algorithm and a least squares algorithm. In UILoc, we have also designed a reliable model to reduce the landmark correction error. Experimental results show that the UILoc can provide accurate positioning, the average localization error is about 1.1 m in the steady state, and the maximum error is 2.77 m.

High precision nodes localization algorithm based on optimal dual-frequency comparison ranging

ABSTRACTIndoor localization plays an important role in medical treatment and industrial application. Dual-frequency phase-comparison method is widely applied to indoor localization. However, frequency was randomly chosen in most current dual-frequency phase-comparison schemes, which reduced the ranging accuracy. Therefore, an IL-ODFC (indoor localization scheme based on optimal dual-frequency comparison ranging) scheme was proposed. An indoor localization system and a wireless sensor nodes based on dual-phase measurement were designed and established in order to form target nodes and reference nodes and send their communication results to the location engine of indoor localization system. Firstly, reasons for errors of traditions dual-frequency phase-comparison ranging in IL-ODFC scheme was analyzed; then, the concept of frequency domain and one-time linear regression idea was introduced; next, LS (least square) method was used to choose optimal difference frequency in order to obtain accurate ranging data. After obtaining distances among three reference nodes, LS was used to estimate the location of the target nodes. The simulation result shows that the proposed IL-ODFC scheme can effectively improve location accuracy.

Accurate Location Tracking From CSI-Based Passive Device-Free Probabilistic Fingerprinting

The research on indoor localization has received great interest in recent years. This has been fueled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system that adopts channel state information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for multiple input multiple output orthogonal frequency-division multiplexing PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-the-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of nonequipped individuals.

Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks

Localization of mobile sensors is an important research issue in wireless sensor networks. Most indoor localization schemes focus on determining the exact position of these sensors. This paper presents a zoning-based tracking technique that works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the mobile sensor in a real time tracking process. The proposed method creates a belief functions framework that combines evidence using the sensors mobility and observations. To do this, a mobility model is proposed by using the previous state of the sensor and its assumed maximum speed. Also, an observation model is constructed based on fingerprints collected as Wi-Fi signals strengths received from surrounding access points. This model can be extended via hierarchical clustering and access point selection. Real experiments demonstrate the effectiveness of this approach and its competence compared with state-of-the-art methods.