Indoor Positioning Method Research Articles

Indoor and Outdoor Seamless Positioning Method Using UWB Enhanced Multi-Sensor Tightly-Coupled Integration

As one of the main methods in positioning and navigation, the Global Navigation Satellite System (GNSS) is widely applied in many areas, such as road engineering, automobile navigation and many other outdoor applications. In addition to an outdoor location-based service, there are many needs for indoor positioning. However, GNSS is hard to position indoors due to signal blockage, and outdoor–indoor transition areas where multipath effects are unavoidable and signal interference is severe. This article proposes a GNSS/INS/UWB tightly-coupled integration positioning methodology to address the problem of accurate positioning in GNSS-difficult areas and aims to provide seamless positioning in different scenarios. In indoor environments, the system uses the UWB/INS tightly-coupled integration positioning mode and periodically corrects the INS errors with range measurements obtained from the UWB transmitting unit to achieve centimeter-level accuracy. In outdoor GNSS-difficult areas like outdoor–indoor transition areas, a GNSS/INS/UWB tightly-coupled integration positioning system is proposed to ensure positioning accuracy continuity and further improve the reliability of the system. To evaluate the positioning performance of the proposed UWB enhanced multi-sensor tightly-coupled integration system, an experiment was conducted in a high-rise building. The experiment results show that the proposed integrated positioning system can provide seamless and accurate positioning results in both indoor and outdoor–indoor transition areas, with DRMS of 5.25 cm and RMSE of 10.18 cm.

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints.

Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Method of Enhancing the Accuracy of Indoor Positioning (RSSI to UL-AOA)

Nowadays, with the rapid development of urbanization worldwide, the indoor area has become complex. Even with floor maps and guidance signs, people waste time on self-location in an unfamiliar building for the reason that the mature location services like GPS are limited as outdoor positioning series, and the method of indoor positioning is relatively missing or rough. Therefore, the better indoor positioning systems are in a highly command. The indoor positioning system has the goals for realizing the location marked and tracking of people and objects in indoor scenes. This paper focused on the main positioning method in use, which is distance-based positioning algorithm. Then, the advantages and disadvantages of the mainstream algorithms RSSI and AOA were discussed. At last, by comparing the current research, the author put forward an optimized indoor positioning algorithm based on AOA, which may become the mainstream in the future.

An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

Nowadays, people’s demand for indoor location information is more and more, which continuously promotes the development of indoor positioning technology. In the field of indoor positioning, fingerprint based indoor positioning algorithm still accounts for a large proportion. However, the operation of this method in the offline stage is too cumbersome and time-consuming, which makes its disadvantages obvious, and requires a lot of manpower and time to sample and maintain. Therefore, in view of this phenomenon, an improved algorithm based on nearest neighbor interpolation is designed in this paper, which reduces the measurement of actual sampling points when establishing fingerprint map. At the same time, some simulation points are added to expand fingerprint map, so as to ensure that the positioning error will not become larger or even better. Experimental results show that this method can further improve the positioning accuracy while saving the sampling cost.

Research on the UWB High Precision Indoor Positioning Method with the Heterogeneous Information Constraints

There are many obstacles in the UWB indoor positioning, such as installation location limitation of base stations, non-line-of-sight and so forward. In this paper, the high-precision indoor positioning model was discussed, and then the UWB indoor positioning method was given based on the heterogeneous data constraints, such as PDR, map and vision. Three indoor positioning models, the kinematic adaptive robust EKF UWB model based on the gain matrix, the UWB/PDR/Map coupled model, and the UWB/Vision fusion model were built and assessed, respectively. Afterward, the precision and the potential application scenarios of the three models were discussed via the practical tests. The test results showed that, with our method, the overall positioning accuracy reached around ¡À0.2 m under the conditions of the full or partial UWB signal coverage, available or interrupted line-of-sight, or undergoing other situational challenges such as the sparse texture and the continuous variation of the light strength.

Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction

An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel‐based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

IBeacon Indoor Positioning Method Combined with Real-Time Anomaly Rate to Determine Weight Matrix.

This paper proposes an indoor positioning method based on iBeacon technology that combines anomaly detection and a weighted Levenberg-Marquadt (LM) algorithm. The proposed solution uses the isolation forest algorithm for anomaly detection on the collected Received Signal Strength Indicator (RSSI) data from different iBeacon base stations, and calculates the anomaly rate of each signal source while eliminating abnormal signals. Then, a weight matrix is set by using each anomaly ratio and the RSSI value after eliminating the abnormal signal. Finally, the constructed weight matrix and the weighted LM algorithm are combined to solve the positioning coordinates. An Android smartphone was used to verify the positioning method proposed in this paper in an indoor scene. This experimental scenario revealed an average positioning error of 1.540 m and a root mean square error (RMSE) of 1.748 m. A large majority (85.71%) of the positioning point errors were less than 3 m. Furthermore, the RMSE of the method proposed in this paper was, respectively, 38.69%, 36.60%, and 29.52% lower than the RMSE of three other methods used for comparison. The experimental results show that the iBeacon-based indoor positioning method proposed in this paper can improve the precision of indoor positioning and has strong practicability.

Wireless Network Indoor Positioning Method Using Nonmetric Multidimensional Scaling and RSSI in the Internet of Things Environment

Aiming at the problem that the indoor target location algorithm based on received signal strength (RSSI) in the IoT environment is susceptible to interference and large fluctuations, an indoor localization algorithm combining RSSI and nonmetric multidimensional scaling (NMDS) is proposed (RSSI- NMDS). First, Gaussian filtering is performed on the received plurality of sets of RSSI signals to eliminate abnormal fluctuations of the RSSI. Then, based on the RSSI data, the dissimilarity matrix is constructed, and the relative coordinates of the nodes in the low-dimensional space are obtained by NMDS solution. Finally, according to the actual coordinates of the reference node, the coordinate transformation is performed by the planar four-parameter model, and the position of the node in the actual coordinate system is obtained. The simulation results show that the proposed method has strong anti-RSSI perturbation and high positioning accuracy.

Robot-Based Indoor Positioning of UHF-RFID Tags: The SAR Method With Multiple Trajectories

This article presents the application of the synthetic aperture radar (SAR) localization method for indoor positioning of ultrahigh-frequency (UHF)-radio frequency identification (RFID) tags when the robot-mounted reader antenna moves along multiple trajectories. By properly combining the phase data associated with a set of multiple paths, the whole length of the combined synthetic apertures enlarges, and then, the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available, and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through numerical analysis by considering different configurations of the multiple apertures and different measurement uncertainty sources. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system.

Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration

WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m.

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor.

The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.

A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction

High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.

A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals.

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

철도역사의 실내 위치측위를 위한 지구자기장 측정시험 및 특성 분석

In recent years, railway station has changed into a complex station connecting with other routes, so there are many difficulties for railway station users to find their destination in the railway station. Especially, the mobility handicapped has difficulty in finding a destination point. Accordingly, a variety of services for improving mobility are required within the stations of railway station users including the handicapped, these services basically require the indoor positioning technology of the railway station. In this paper, the applicability of the fingerprinting method based on the measurement of the geomagnetic field strength, which is the most economical and accurate method among the methods for indoor positioning in the railway stations, is analyzed through experiments. Experiments were carried out in the platform section and the waiting room section of the metro station with two floors underground, and In order to confirm the change of the geomagnetic signal according to the running of the train, various cases such as the case where the train entered the platform, the case where the train was not at the station were measured and et al. Through these experiments, this paper confirmed that the fingerprinting method based on the geomagnetic field signal, which is widely used in the general industry, is applicable to the indoor positioning of the railway station.

철도역사의 실내 위치측위를 위한 지구자기장 측정시험 및 특성 분석

In recent years, railway station has changed into a complex station connecting with other routes, so there are many difficulties for railway station users to find their destination in the railway station. Especially, the mobility handicapped has difficulty in finding a destination point. Accordingly, a variety of services for improving mobility are required within the stations of railway station users including the handicapped, these services basically require the indoor positioning technology of the railway station. In this paper, the applicability of the fingerprinting method based on the measurement of the geomagnetic field strength, which is the most economical and accurate method among the methods for indoor positioning in the railway stations, is analyzed through experiments. Experiments were carried out in the platform section and the waiting room section of the metro station with two floors underground, and In order to confirm the change of the geomagnetic signal according to the running of the train, various cases such as the case where the train entered the platform, the case where the train was not at the station were measured and et al. Through these experiments, this paper confirmed that the fingerprinting method based on the geomagnetic field signal, which is widely used in the general industry, is applicable to the indoor positioning of the railway station.

A regression model-based method for indoor positioning with compound location fingerprints

ABSTRACT This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations. It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning.

Unsupervised Indoor Positioning System Based on Environmental Signatures.

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.

Design of a Smartphone Indoor Positioning Dynamic Ground Truth Reference System Using Robust Visual Encoded Targets

Smartphone indoor positioning ground truth is difficult to directly, dynamically, and precisely measure in real-time. To solve this problem, this paper proposes and implements a robust smartphone high-precision indoor positioning dynamic real-time ground truth reference system using color visual scatter-encoded targets based on machine vision and photogrammetry. First, a kind of novel high-precision color vision scatter-encoded patterns with a robust recognition rate is designed. Then we use a smartphone to obtain a sequence of images of an experimental room and extract the base points of the color visual scatter-encoded patterns from the sequence images to establish the indoor local coordinate system of the encoded targets. Finally, we use a high-efficiency algorithm to decode the targets of a real-time dynamic shooting image to obtain accurate instantaneous pose information of a smartphone camera and establish the high-precision and high-availability smartphone indoor positioning direct ground truth reference system for preliminary real-time accuracy evaluation of other smartphone positioning technologies. The experimental results show that the encoded targets of the color visual scatter-encoded pattern designed in this paper are easy to detect and identify, and the layout is simple and affordable. It can accurately and quickly solve the dynamic instantaneous pose of a smartphone camera to complete the self-positioning of the smartphone according to the artificial scatter feature visual positioning technology. It is a fast, efficient and low-cost accuracy-evaluation method for smartphone indoor positioning.

An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhancement positioning method based on the nearest neighbor algorithm is proposed. The distribution of the RSS samples recorded from several Access Points (APs) are used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest K th Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods.