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Driven by the burgeoning demand for location-based services, the quest for robust indoor positioning systems (IPSs) becomes increasingly imperative. Currently, fingerprint-based approach has emerged as the predominant technique for IPSs because of its ability to deliver accurate location estimations without necessitating line-of-sight transmission from access points (APs). This approach involves constructing a radio map based on the wireless signal properties and employing matching algorithms, such as the nearest neighbor, to predict the position of target. Despite its merits, this technique has its drawback, since received signal strength (RSS) are heavily impacted by the indoor environment, leading to potential discrepancies between real-time signal measurements from the target’s mobile device and RSS vector from the fingerprint database when environmental changes occur. To tackle this issue, this work proposes a novel adaptive weighted AP (AWA) algorithm that exploits newly acquired RSS vector from a small subset of the reference points to dynamically assign weights to different APs based on the impact of environmental changes on their RSSs. By harnessing these weights to compute the distance between the RSSs at unknown location and those in the fingerprint database, two novel IPSs are developed to effectively identify the nearest neighbors of the target and subsequently predict the target’s location. Remarkably, results reveal that the AWA-based IPSs achieve a substantial enhancement of 40% in average positioning error over the baseline counterparts without AWA in dynamic indoor environments by leveraging merely 9.37% of the reference points to measure the updated RSSs.

In this paper, we propose a novel method for dynamic positioning using a single passive RFID tag to obtain its position through phase. This method addresses the phase integer ambiguity by introducing a robust phase unwrapping algorithm, solves the initial position problem using the maximum probability algorithm, and achieves accurate dynamic positioning through linear intersection. Experimental results demonstrate that our proposed method supports both static and dynamic positioning (speed

WiFi fingerprinting is one of the most widely used techniques for indoor positioning systems. However, existing fingerprinting datasets came in different shapes and forms with varying levels of information without any standardised format. They were also dispersed across multiple platforms, making it challenging for new researchers to identify and access a suitable dataset to evaluate their own positioning systems. To address this challenge, this paper provides a comprehensive review of more than 50 publicly available WiFi fingerprinting datasets. We examine the most critical elements for fingerprinting, including the size and location of the testbed, the WiFi signal input, the number of locations, the temporal and spatial intervals of data collection, the positioning performance, and more. Surprisingly, it was observed that a large number of reference and access points, the use of 3D coordinates, denser sampling grid, and higher data collection frequencies do not always guarantee improved performance as often reported in the literature. The paper also outlines current challenges, and proposes guidelines for creating new WiFi fingerprint datasets.

Accurate indoor positioning is critical to a variety of use cases including tracking, proximity, mapping, and navigation. Existing positioning methods are either inaccurate (e.g. RSSI-based ranging), impractical (e.g. fingerprinting), or uncommon (e.g. Ultra-Wide Band/UWB). WiFi Round-Trip Time (RTT) marries the ubiquity of WiFi infrastructure with the accuracy of such ranging mechanisms as those used in UWB. We present WhereArtThou, a commercial-grade, plug-and-play indoor positioning solution (IPS) based on WiFi RTT. Our base algorithm uses an extended Kalman filter with a random walk motion model (EKF-RW) which relies solely on RTT distance measurements. We propose two EKF components to enhance positioning accuracy. First, as distance measurements can further be fused with inertial sensor readings, we propose a step-and-heading-based filter (EKF-SH) when such readings are available. We devise a method to fit the non-Gaussian step error with a Gaussian random variable to remain within the computationally-efficient Kalman filtering framework. Second, we define a distance-dependent measurement model to match the true statistics of the measurement noise and approach optimal position estimation. Moreover, we measure the gain from the proposed enhancements not only through the almost-exclusively-used error metric in the indoor positioning literature, the Euclidean distance and its variations, but also through metrics commonly used in satellite and maritime navigation, the cross-track and along-track errors, in addition to a set of metrics of our own definition. Finally, we show that RTT is highly susceptible to the human body holding the device measuring it, and we case-study the impact of human-body blockage on positioning and ranging errors. We test our algorithms on over 18 hours of walking data collected on different devices, in different locations, and with different users, and we observe that the EKF-RW and EKF-SH achieve 90th percentile distance errors of 1.65 m and 1.45 m, and 90th percentile cross-track errors of 0.85 m and 1.55 m, making our solution primed for commercial deployment.

Accurate indoor positioning is becoming increasingly important, especially in highly automated industrial environments with robots. In addition, LED-based lighting is also being used more and more frequently in such application fields. In the present work, the possibility to exploit the LED lighting infrastructure with a novel approach for implementing an accurate indoor positioning system is investigated. For this purpose, a demonstrator luminaire LEDPOS is proposed and evaluated that combines visible light sensing based on backscattered reflections to accurately estimate the two-dimensional position of a retroreflective foil at the floor while providing simultaneously an unimpaired room illumination. In particular, the same LED elements are shared for illumination and for the sensing functionality. Furthermore, the algorithm for data evaluation and position determination is based on a machine learning approach that is implemented on the edge in the luminaire. Thus, the presented approach allows for a simple and cost-efficient implementation in different applications. The experimental characterization of the LEDPOS demonstrator in a real-world scenario shows that a very good positioning accuracy can be achieved, in which the average error for the two-dimensional position of the retroreflective foil within an area of 0.64 m2 remains in the range of 3 cm.

With the ongoing development of Indoor Location-Based Services, the location information of users in indoor environments has been a challenging issue in recent years. Due to the widespread use of WiFi networks, WiFi fingerprinting has become one of the most practical methods of locating mobile users. In addition to localization accuracy, some other critical factors such as latency, and users’ privacy should be considered in indoor localization systems. In this study, we propose a light Convolutional Neural Network-based method for edge devices (e.g. smartphones) to overcome the above issues by eliminating the need for a cloud/server in the localization system. The proposed method is evaluated for three different open datasets, i.e., UJIIndoorLoc, Tampere and UTSIndoorLoc, as well as for our collected dataset named SBUK-D to verify its scalability. We also evaluate performance efficiency of our localization method on an Android smartphone to demonstrate its applicability to edge devices. For UJIIndoorLoc dataset, our model obtains approximately 99% building accuracy, over 90% floor accuracy, and 9.5 m positioning mean error with the model size and inference time of 0.5 MB and $51~\mu $ s, respectively, which demonstrate high accuracy in range of state of the art works as well as amenability to the resource-constrained edge devices.

Achieving precise localization in industrial settings presents significant challenges due to dynamic movements, complex layouts, and harsh environmental conditions that cause signal interference and reflections. This requires developing advanced indoor positioning systems that can handle these challenges and perform reliably even in the presence of dynamic movement. In this paper, a novel LoRa-based indoor positioning system designed for dynamic motion in industrial environments is presented. The proposed system integrates LoRa technology with a fingerprinting approach that involves fingerprint collection using the constant motion method and leverages a two-layer Deep Gaussian Process Regression (DGPR) model to overcome the non-linearity characteristics of signal propagation. Through testing on static and motion datasets, it was observed that collecting data in motion yields superior results for tracking dynamic objects. Furthermore, temporal-based enhancements like Temporal Weighted RSSI Averaging and Kalman filtering were introduced. These techniques effectively mitigate RSSI temporal variations and improve the reliability of position estimates. The experimental results, conducted in a real industrial environment, demonstrate that the proposed system achieves a mean positioning error of 1.94 meters and a 90th percentile error of 3.28 meters. These findings highlight the potential of combining LoRa technology with advanced machine learning algorithms and filtering techniques to achieve precise and reliable indoor tracking.

Indoor private (local) wireless networks are considered key enablers for the industrial Internet of Things (IIoT). To ensure that a private network satisfies its own requirements and guarantees coverage, it is important to accurately measure the radio performance at the accurate position to further optimize the network. Additionally, as carrier frequencies increase, cellular network-based positioning becomes increasingly important, as it enables more accurate positioning. For example, centimeter-level accuracy is expected in beyond 5G systems owing to the sub-THz band carrier frequency. To accurately compare and assess various cellular network-based positioning methods, positioning devices, which can move along the same trajectory more accurately than at the centimeter-level, are required. To this end, we implemented a three-dimensional (3D) positioning device developed from open-source hardware, which is generally used to implement computer numerical control (CNC) machines and 3D printers. The implemented 3D pointing device is cost-effective and portable and can be freely configurable and resized in one, two, and three dimensions owing to the use of open-source hardware. The received signal strength indicator (RSSI) employing the LoRa module can be obtained using the implemented device. Using a very low carrier frequency system, it is possible to accurately measure the RSSI variations due to multipath propagation. After demonstrating the reproducibility of the data, the effectiveness of the implemented device was confirmed by showing that the RSSI variation and fading correlation coefficient are close to the Rayleigh fading variation.

Camera localization involves the estimation of the camera pose of an image from a random scene. We used a single image or sequence of images or videos as the input. The output depends on the representation of the scene and method used. Several computer vision applications, such as robot navigation and safety inspection, can benefit from camera localization. Camera localization is used to determine the position of an object on the camera in an image containing multiple images in a sequence. Structure-based localization techniques have achieved considerable success owing to a combination of image matching and coordinate regression. Absolute and relative pose regression techniques can provide end-to-end learning; however, they exhibit poor accuracies. Despite the rapid growth in computer vision, there has been no thorough review of the categorization, evaluation, and synthesis of structures and regression-based techniques. Input format and loss strategies for recurrent neural networks (RNN) have not been adequately described in the literature. The main topic is indoor camera pose regression, which is a part of the camera localization techniques. First, we discuss certain application areas for camera localization. We then discuss different camera localization techniques, such as feature and structure-based, absolute and relative pose regression techniques, and simultaneous localization and mapping (SLAM). We evaluated the frequently used datasets and qualitatively compared the absolute and relative camera pose estimation approaches. Finally, we discuss potential directions for future research, such as optimizing the computational cost of the features and evaluating the end-to-end characteristics of multiple cameras.

Accurate indoor positioning is crucial for many location-based services, but GPS accuracy is significantly reduced due to issues such as signal penetration and accuracy in indoor scenarios. In contrast, indoor Wi-Fi positioning is emerging as a promising alternative in the field. This study proposes a model that combines the k-nearest neighbor algorithm with the dynamic time regularization distance metric for indoor Wi-Fi positioning, and investigates methods for optimizing this model. The traditional K-nearest neighbor algorithm usually uses Euclidean distance for distance calculation, which has the disadvantage of being affected by the length of the signal sequence, resulting in inaccurate calculation of the distance between adjacent points with different time intervals. The dynamic time regularization is more suitable for signals of different lengths like Wi-Fi, which can bend the time axis to make the alignment of two Wi-Fi sequences more accurate. Using DTW as the distance measure of KNN is DTW-KNN. In addition, to enhance the model’s ability to handle large-scale data sets, We use Gaussian sum matrices instead of the distance matrix of the traditional dynamic time regularization algorithm. Once again, the standard deviation sigma of the Gaussian distribution and the distance hyperparameters of the K-nearest neighbors are optimally chosen for the most suitable values of Wi-Fi signals. Finally, a fast recognition model based on intermittent downsampling and an accurate recognition model with complete sampling are designed to cope with the focus on real-time and accuracy in different scenarios. These two models can achieve 95.3% and 98% accuracy, respectively, on the public dataset (Wireless Indoor Localization) of indoor Wi-Fi localization.

Camera based visible light positioning (VLP) can be promising for indoor positioning applications powered by the widespread use of light emitting diodes (LEDs) in lighting applications. Most of the VLP works in the literature have either implemented two-dimensional (2D) indoor positioning or assumed that the height of the ceiling is either fixed or known. In this paper, the real-time height of the ceiling from the receiver is taken into consideration to make the three-dimensional (3D) positioning possible in practical use case scenarios. A low cost light detection and ranging (LIDAR) sensor has been used to measure the height of the ceiling in real time to estimate the location in 3D space using a single LED transmitter. Additionally, though camera based indoor positioning using the rolling shutter effect, offers high accuracy, its reliability at long distances and robustness against the line of sight (LOS) blockage are still questionable. In this paper, a low resolution, high frame rate camera is proposed for an indoor positioning system with centimeter-scale accuracy. Direct detection has been utilized instead of the rolling shutter effect thus making the system more robust and reliable. A gravity virtual sensor has been used to compensate for the error caused by the tilt of the smartphone. The average 3D positioning error in the proposed VLP system is 6.8 cm. The positioning latency of the proposed system is 54 ms. To the best of the Authors’ knowledge, this is the first reported truly 3D VLP system that works without any prior knowledge of the ceiling height and irrespective of LED shape, size, and arrangement using a single LED. The proposed system is also robust against any partial light blockage by the user’s body and it can also provide coarse location information without any direct LOS between the LED and the camera using reflected lights from the surroundings.

This paper proposes the $2.45~GHz$ planar Beam Steering Antenna Array for Indoor Positioning Systems. The antenna array consists of a phase shifter based on a $4\times 4$ Butler matrix, and an array of four dipole Yagi antennas. By choosing one of four beams using a switching controller, the main beam of the antenna can steer in four directions from +37°, −12°, +12° to −36°. The antenna has a narrow fan beam with a beamwidth in the azimuth plane of from +21.5° to +24.5° and in the elevation plane of approximately 90° which effectively minimizes multipath signals. These advantages are suitable for positioning systems to increase location accuracy. The antenna achieves a peak gain of from 9.1 to $9.8~dBi$ in four directions, a wide band of $400~MHz$ . The fully electronically steerable antenna prototype has been designed using CST software, simulated based on the finite element method, and fabricated on the RO4003C substrate as well as measured. The antenna is implemented in the Indoor Positioning System using three different location methods, including trilateration, triangulation, and fingerprinting, to highlight the antenna’s advantages in improving location accuracy with the ratio between the mean location error and area of 0.051, 0.033, and 0.023 respectively.

In this paper, we consider wireless positioning using Received Signal Strength (RSS) fingerprinting. To obtain good accuracy, this technique requires a database containing a high density of up-to-date fingerprints. However, as acquiring fingerprints through training is labor intensive and the indoor topology is subject to changes, a high density fingerprint database cannot always be obtained. On the other hand, the time to retrieve data from a database with high density can be too high for real-time positioning. To tackle these issues, we introduce the Hierarchical Positioning Algorithm (HPA). In this algorithm, we divide the database into a number of sub-databases with different densities, each containing a sufficiently small number of fingerprints to reduce the data retrieval time. The algorithm starts with a coarse estimate at the highest level, and gradually improves the accuracy in going to the lowest level. This HPA technique requires the construction of sub-databases containing fingerprints that are properly selected to obtain the wanted level of accuracy. This paper considers two algorithms to construct the database: the Minimum Distance Algorithm (MDA) to select the reference points, and the Local Gaussian Process (LGP) algorithm to determine the RSS values at the selected reference points. Simulation results show that the hierarchical algorithm, combined with MDA and LGP to construct the sub-databases, is a fast algorithm that can achieve high accuracy, even with a database having a variable density of fingerprints.

Fingerprint-based indoor positioning systems (F-IPS) may provide inexpensive solutions to GPS-denied environments. Most F-IPSs adopt traditional machine learning for position prediction, resulting in low accuracy. Deep neural networks (DNN) were recently employed for F-IPSs to minimize positioning errors. Nevertheless, a DNN-IPS fails to guarantee high accuracy in dynamic environments as it is sensitive to changes in the input data. A convolutional neural network (CNN) is recommended to replace DNN due to its capability to learn the overall topology of fingerprinting images and capture highly abstract features. Due to the convolution process and image representation, CNN-IPS incurs prohibitive storage and computational requirement for implementation on resource-limited devices. This paper incorporates knowledge distillation (KD) into CNN-IPS to distil knowledge from large deep CNNs into small CNNs. The pre-trained teacher network uses the soft probability output where the score vector from the trained network is converted into a probability distribution, which is softened by the temperature hyperparameter, leading to a more simplified model. Based on the numerical results, KD-CNN-IPS manifests better localization performance where 79.84% of the positioning errors are within 2 meters while its testing time is only 79.68% of that of the teacher model. Compared to the CNN-IPS, KD-CNN-IPS with precisely the same architecture and size could achieve a performance improvement of 13.65% in terms of the average positioning error.

In many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting. Research in this area has been faced with a number of challenges, leading to the proposal of a number of localisation algorithms, sampling strategies, benchmark datasets, and representations of building information. This proliferation made the modeling of the indoor positioning domain quite hard from both a theoretical and a practical point of view. In this paper, we propose a general and extensible framework, based on a relational database, that pairs fingerprints with building information. We show how the proposed system successfully deals with a number of problems that affect indoor positioning, supporting a large set of relevant tasks. The source code of the framework is available online, as well as an implementation of it, that provides an interactive open repository of indoor positioning data.

Visual simultaneous localization and mapping (vSLAM or visual SLAM) is an important technique for mobile robot localization in the global navigation satellite system denied (GNSS-denied) environments. However, the positioning accuracy could become unbearable due to the lack of image feature points when the robot navigates in a spacious indoor space. The drifting errors accumulated over time are generally inevitable and need to be mitigated by more sophisticated loop-closure algorithms. In this paper, we propose a drift-free visual SLAM technique for mobile robot localization by integrating the ultra-wideband (UWB) positioning technology. The basic concept is to utilize the global constraint of the UWB positioning to reduce the locally accumulated errors of visual SLAM localization based on the extended Kalman filtering (EKF) framework. In our experimental results, various SLAM approaches are performed in the indoor scenes, and the evaluation and comparison have demonstrated the feasibility of the proposed localization technique. By the integration of UWB positioning, the overall drift error of the robot navigation is reduced for more than 50%.

Indoor localization technologies are actively investigated to realize location-based applications in various environments, and indoor localization methods based on whether the received signal strength indicator (RSSI) is less than a threshold have been proposed previously. Such a proximity/non-proximity binary value is used in digital contact tracing applications to reduce the coronavirus disease effects. We proposed two indoor pedestrian localization methods based on contact information using bluetooth low energy (BLE) beacons, namely multilateration and cooperative localization. This study attempts to demonstrate the effectiveness of the proposed methods using only contact information. Through simulation experiments, we found that the proposed methods can achieve comparable accuracy to existing methods when the attenuation model is accurate. The difference in average localization error was 0.1 m between the proposed method 1 and range-based method, and 0.2 m between the proposed method 2 and fingerprinting method. We confirmed that the proposed methods using only contact information are robust against environmental changes even when the attenuation model is inaccurate. We consider that these contributions have added a new perspective on the use of contact information in the field of indoor localization, which aims to realize power-saving and cost reduction.

The continuous positioning of a mobile user equipment (MU) in a prompt way is a fundamental requirement in several indoor applications related to ubiquitous computation, assisted living environments, and security/surveillance systems. Motivated by this fact, the current contribution reports a low-complexity visible light positioning (VLP) method suitable for indoor environments in the Big Data era. The proposed architecture consists of multiple light-emitting diodes (LEDs) as light sources and an MU equipped with a photodiode (PD). To guarantee higher spectral efficiency, the LEDs emit sinusoidal waveforms at slightly different predetermined frequencies. The light intensity received at the PD from every LED is continuously estimated after applying a short-time Fourier transformation. To this end, a grid-based numerical technique is proposed to recover the unknown MU position. Additionally, a closed-form solution is presented for the particular case of three LEDs that overrides the need for training points in the grid arrangement. Further, a method for handling noisy signals is proposed, based on averaging the calculated positions from densely overlapping received signals. Finally, a Kalman filter is employed as a post-processing precision improving tool. The proposed VLP efficiency is quantified through respective Monte Carlo simulations that allow the setting of different LED frequencies, received signal processing parameters, MU speed, and noise levels. The results reveal that the suggested approach is robust with significant tolerance in high ambient light levels, computationally efficient, and exhibits low positional error. Finally, to evaluate the performance improvements the new method introduces, we make comparisons against widely-used fingerprint approaches.

In this paper, we introduce an ultra-high frequency radio frequency identification (UHF-RFID) mobile robot platform that is capable of performing fully autonomous inventory taking and stocktaking by providing three-dimensional (3D) product maps and thus making possible the concept of a smart warehouse. The proposed novel hardware architecture consists of an eight-channel UHF-RFID-listener for parallel signal phase recovery, including two different carrier leakage suppression circuits and a correlation decoder for each channel for the tag signal, which can handle a backscatter link frequency (BLF) deviation of up to 22 % to decode the tag data. The system also uses eight parallel channels for multiple-input multiple-output (MIMO) localization. For the system evaluation we labeled clothes stored in a warehouse with tags and generated their product map. The proposed localization algorithm is based on a synthetic aperture radar (SAR) MIMO approach that needs exact knowledge of the antenna positions and, therefore, of the driven trajectory. This position is provided by the robot, which takes advantage of a simultaneous localization and mapping (SLAM) algorithm, determining the position with 1 cm accuracy while generating two-dimensional (2D) maps of the surroundings. We placed ten tags at known positions to assess the system's performance and were able to locate these tags within a root mean square error (RMSE) of 1.45 cm in 3D.

The wide use of mobile devices introduced several new services for the consumer market which are collectively called location-based services, the name being indicative of the significance of the consumer position. Consequently, a rich variety of positioning technologies have been adopted to provide and enhance user location information. The mass deployment of Wi-Fi access points (APs) and the ubiquity of the magnetic field data make them attractive candidates for indoor positioning. Additionally, the availability of embedded magnetic and Wi-Fi sensors in smartphones helps to achieve positioning without additional infrastructure. Even though Wi-Fi and magnetic field data offer complementary characteristics for enhancing positioning accuracy, several challenges for these technologies remain unresolved. However, the lack of publicly available datasets for the magnetic field and Wi-Fi makes it very difficult to extensively investigate these characteristics. Also, the proposed approaches cannot be tested on common benchmark datasets to analyze the results of the state-of-the-art approaches. To resolve these issues, this study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five different smartphones used by four different users, both female, and males. Different path geometries are followed in different multi-floor buildings which are physically separated, comprising both small and large areas. Besides, three different orientations of the smartphone are considered for data collection covering corridors, halls, and laboratories. The data from the stairs help to test ‘stairs up’ and ‘stairs down’ events and approaches aiming at multi-floor positioning can be tested with the provided dataset.

The need for an accurate indoor positioning system has rapidly increased with the development of large complex malls and underground spaces. As signals from the global positioning system cannot be received inside buildings, only approximate locations can be estimated using Wi-Fi routers or cellular base station information, and exact locations cannot be determined. Therefore, a pedestrian dead reckoning (PDR) scheme using several sensors is suggested in this work. However, this scheme requires users to hold their smartphones in a specific manner; furthermore, user-dependent parameters, such as height and step length, are necessary because the sensor parameters vary. This study uses deep-learning algorithms to overcome the limitations of the existing smartphone-based PDR scheme. A convolutional neural network algorithm is used to classify the smartphone positions; then, appropriate sensor data are selected and adjusted. The long short-term memory algorithm is used to estimate the user step length. Although the PDR performance is enhanced using the deep-learning algorithm, accumulated error is unavoidable because the algorithm traces the relative position with reference to the original location. Therefore, optical camera communication is introduced to provide the reference location and periodically compensate for the accumulated PDR error. The proposed algorithm is experimentally demonstrated, and its results are obtained and analyzed.

Some of the researches on indoor positioning have been conducted, but there are still many constraints on indoor positioning approaches. Among these approaches, ultra-wideband (UWB) provides a fast and precise positioning performance but requires a sufficient infrastructure and a clear line-of-sight (LOS) channel. However, inertial sensor-based pedestrian dead reckoning (PDR) operates without infrastructure, but it requires position initialization and has error drift problems. In this study, we propose a hybrid positioning system that fully combines UWB and PDR to overcome such constraints and improve the positioning performance. This hybrid positioning system uses a Kalman filter (KF) based fusion method that identifies non-line-of-sight (NLOS) environments and mitigates UWB errors through PDR. We also evaluated the proposed system implemented using practical testbed devices at indoor environments classified as LOS, weak NLOS, and hard NLOS. The evaluation results showed that the proposed system significantly improves the positioning performance and alleviates the positioning constraints, as compared to the single positioning system. Our system has been designed to be lightweight compared to the existing extended KF-based convergence system, but is more robust to both weak and hard NLOS environments. Eventually, it improved positioning performance by 35.5% than existing hybrid systems in the hard NLOS environments.

With the development of the mobile Internet, the demand for indoor location based services(ILBS) is increasing. Applications and devices in indoor environments can determine their position based on Received Signal Strength Indication(RSSI) of bluetooth Beacons. However, because of multipath effects, shadow fading and the blockage of obstacles, RSSI values are unstable for positioning. The propagation model are various in different environments, which have caused great difficulty in high-precision indoor positioning. In this paper, a novel positioning algorithm based on cellular network and bluetooth Beacon's RSSI is proposed for ILBS. The whole algorithm is divided into two parts: Offline Preparation and Online Positioning. During Offline Preparation, Beacon node is cellular networked in regular quadrilateral; a universal ranging model is derived from traditional ranging model for high-precision positioning. During Online Positioning, missing RSSI values are replaced firstly; next, Outliers Removed Median-Kalman Filter is used to process RSSI, which makes the signal smoother to range; then, the cell in which the mobile terminal is located is determined according to the strongest four RSSI values; after that, using filtered RSSI, distances without height influence could be calculated; finally, Weighted Multi-Point Positioning Algorithm With Cellular Network Assistance is used to calculate the real-time location of the mobile terminal. Experiment results show that our algorithm achieves average accuracy in 0.3m-0.5m and limits the maximum error within 0.8 m.

Enhancing the accuracy of indoor visible light positioning systems with simple, real-time, and stable methods is one of the interesting challenges in recent research. In this paper, a relatively minor mean positioning error of 8 mm and a 42-52% improvement in computational time could be achieved within a real space of 1.2 m $\times1.2$ m $\times1.2$ m by transcending the serious limitations of the traditional k-nearest neighbors (KNN) algorithm. These disadvantages (slow execution time, high error formation) are a result of finding the nearest neighbors from all the fingerprints, averaging the Euclidean distances, and the excessive passivity of the K value. To overcome the above limitations of KNN, we proposed a maximum received signal strength recognition (MRR) technique and weighted optimum KNN (WOKNN) algorithm, which is a combination of optimum KNN (OKNN) and weighted KNN (WKNN). While MRR was used to reduce the computational time, WOKNN was used to solve the remaining problems. Specifically, OKNN was used to automatically determine the best number of nearest neighbors for each position in the area under consideration, and WKNN helped improve the errors that come from the Euclidean distance averaging process. Based on positive experimental results and a meaningful comparison with various versions of KNN, we demonstrated that the improved conventional KNN algorithm can achieve very high positioning accuracy and is totally suitable for several specific 2-D indoor positioning applications.

In this article, we propose a new geomagnetic localization scheme, named ILoA, to address error accumulation and global localization. Global localization is a fundamental problem that determines the initial pose under global uncertainty. Moreover, error accumulation using inertial navigation systems (INS) impacts robustness and drift error, making it challenging to achieve reliable estimation. The magnetic field in indoor space generates a unique signature/anomaly, which can be used as a local feature. Earth's magnetic field can be easily influenced by ferromagnetic material from the indoor environment due to its weak intensity. The magnetic field vector measured by a magnetometer depends on the orientation of the sensor, which we term a direction variant. We devise a novel approach to identify location and heading through the direction-variant augmented vector. Since a magnetic field vector under varying poses can produce many different vectors, the geomagnetic map is trained with the transformation. We present experiments in two testbeds, covering open space, showing that the proposed method using the magnetic field vector is efficient for global localization and accuracy compared with a state-of-the-art approach.

The use of the magnetic field data to perform indoor positioning gained an accelerating interest during the last few years. The ubiquity of the magnetic field data makes it a potential candidate for indoor positioning and localization. Additionally, the availability of the embedded magnetic sensor in the smartphones makes it an infrastructure-less approach that requires no additional infrastructure to perform positioning. Despite that, the magnetic field-based positioning is relatively new than those of Wi-Fi, radio frequency identification, and ultra-wideband based approaches and faces many challenges and necessitates a comprehensive investigation of the associated problems for wide applicability. For example, smartphone heterogeneity, mutation of the magnetic field data due to change in the user's height, time, and device, and low discernibility are a few of the problems that require substantial investigation before the practical deployment of the magnetic field data for indoor positioning and localization. This study contributes by endeavoring a comprehensive analysis of the opportunities and challenges of using the magnetic field data for indoor positioning and localization using smartphones. The pros and cons of the magnetic field data are highlighted through the experiments conducted in various indoor environments using multiple smartphones. The research thus helps in understanding the practical considerations for deploying the magnetic field data for indoor positioning and localization.

Visual localization is an accurate and low-cost indoor localization solution. A bottleneck for visual localization is the computation efficiency of continuous image searching and matching. In this paper, an indoor visual localization method is proposed to realize continuous and accurate indoor localization based on image matching. This method uses smartphones to collect multi-sensor data, including video frames and inertial readings. To improve the computation efficiency of the proposed visual localization method, a spatial model is developed to optimize the spatial organization of geo-tagged images in a dataset. Several spatial constraint-based image searching strategies are also designed to further reduce the computation time. Based on the spatial model and spatial constraint-based strategies, a visual localization algorithm is proposed. The experimental results show that the localization errors of the image querying, continuous offline localization and online localization of this method are approximately 0.4 m, 0.7 m and 0.9 m, respectively. This method can achieve an accuracy of 1.3 m, even under a random camera opening condition. The average computation time (i.e.. the average time needed to provide a location estimation result) is approximately 0.59 s. The results indicate that the proposed method can realize efficient and continuous indoor localization with high localization accuracy.

The global positioning system is the most popular outdoor positioning system; however, it is inapplicable to indoor environments. In this study, we created a hybrid algorithm, which applies an artificial neural network to resolve the problems of indoor positioning and a swarm-intelligence algorithm to perform the time-consuming task of adjusting parameters. Experiment results demonstrate that the positioning accuracy of the proposed hybrid algorithm is equivalent to the exhaustive method with search times that are far shorter.

Channel state information (CSI) fingerprint based indoor positioning has been extensively studied. While centimeter-level positioning accuracy has been demonstrated for Wi-Fi systems, only meter-level accuracy was reported for Long-term Evolution (LTE) systems. To investigate whether the centimeter accuracy is achievable or not for LTE systems, we examine the factors that affect the positioning accuracy to guide the implementation of a LTE-CSI fingerprinting system, where a novel phase compensation method is proposed to improve the CSI quality. We demonstrate for the first time that the centimeter-level positioning accuracy is achievable for LTE systems through extensive experiments.

A single-transmitter-based positioning system using an antenna array has been designed and developed to minimize the number of transmitters needed for environments where satellite-based navigation systems are unavailable. One of its suitable applications is for indoor navigation. The user receives the signals from the antenna array and calculates the position using the carrier-phase measurements. A key feature of the system is that the number of cycle ambiguity candidates is geometrically bounded. If the length of the antenna baseline is smaller than half the wavelength, the ambiguity resolution process is unnecessary. To improve the positioning performance for practical use, the antenna baseline must be enlarged. Even in this case, the cycle ambiguity resolution issue can be resolved fast and accurately. For instance, there are only nine candidates according to the setting of the study. To investigate the performance of the system, experiments for a dynamic rover using multiple “pseudolites,” pseudo-satellite transmitters, were conducted. In addition, low-cost inertial measurement unit (IMU) sensors were combined to investigate the improved positioning accuracy. Experimental results showed that positioning root-mean-square errors were 18 cm and 25 cm with IMU and without IMU, respectively, at the scale of a conventional meeting room. All integer-cycle ambiguities were solved accurately during the experiments, and more than 93% were resolved within 5 s.

Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN's network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.

Location-based services (LBSs) for elderly care is a trending topic in smart homes. The key issue is the high accurate positioning for the elderly. Ultra-wideband (UWB) is a centimeter-level positioning accuracy in line-of-sight (LOS) environments. However, most of existing UWB positioning methods need non-line-of-sight (NLOS) identification and compensation, and thus leading to severe deterioration in positioning accuracy in presence of complex indoor environments where the elderly lived. This article proposes a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care. In our positioning framework, we first calculate the phase difference of arrival from PDOA chip integrated in the UWB base stations (BSs) to obtain a coarse location of the elderly, which is further used to distinguish which nearby BSs are in LOS environments and which are in NLOS environments. Then, a PDOA-UWB positioning solution is derived to improve the positioning accuracy of the elderly. Compared with some existing methods, our method achieves higher accuracy with less NLOS compensation, and is easier to be implemented in complex indoor environments for simple practical engineering applications. Experimental results show the efficacy of our proposed method.

Indoor wireless positioning using Bluetooth Low Energy (BLE) beacons have attracted considerable attention from industry and academia given the many advantages of this technology such as low power consumption, low cost, easy layout, high availability, and high precision. However, the indoor positioning accuracy always suffers from non-line of sight (NLOS) propagation, stemming from the frequently occurring instances of reflection, refraction, or scattering of BLE radio signals due to the complexity of indoor environments. This article proposes an integrity monitoring (IM) algorithm to detect and eliminate two gross errors simultaneously to solve the adverse effects caused by the NLOS. The logarithmic attenuation model based on the received signal strength indication (RSSI) of BLE realizes positioning by combining trilateration and Least Squares Based on the Taylor expansion (LSBT). Furthermore, the IM based on hypothesis testing is employed to improve the positioning quality andthe users will be alerted in time to avoid risk from positioning accuracy no longer meet user's requirement. The performance of the proposed IM algorithm has been extensively tested by conducting simulation and field experiments. The experimental results show that the IM algorithm significantly improved BLE positioning accuracy as well as the robustness of the positioning system. The 90% average error (1.9143m) in seven groups of single point experiments was reduced by 34.48% over the 90% average error (2.9143m) of the LSBT method after performing IM, and the maximum error during continuous positioning did not exceed 3m after performing IM, which were better than only using LSBT positioning.

A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.

With the increasing demand for location awareness, indoor positioning has gradually become one of the research hotspots. In recent years the indoor positioning research field generally introduced dilution of precision (DOP) in the field of satellite navigation to the optimization of base station (BS) configuration, but in practice, the distribution of DOP and the distribution of errors is far from the same. This paper analyzes the applicability of DOP in the indoor time difference of arrival (TDOA) positioning systems from various aspects. Then, according to the ultrawideband (UWB) ranging error model, the TDOA time-difference measurement error model is derived and used as the BS configuration optimization metric. Finally, the experiments are conducted in 8 m * 8 m * 3 m small-scale scene and 100 m * 30 m * 10 m large-scale scene respectively to compare the positioning accuracy of the three BS configurations under different receiving signal-to-interference ratio (SIR). At the same time, the experiment is conducted in outdoor standard football field to compare the influence of multipath on the positioning accuracy of the three BS configurations under the conditions of two SIR. The experimental results show that the optimized BS configuration with DOP as the optimized target is not the best BS configuration, and the positioning accuracy is not as good as the commonly used cuboid 8-BS configuration in low SIR environment. It proves that DOP cannot be used as a decisive metric for the BS configuration optimization of UWB indoor positioning, but can be used as a reference index, and the optimized BS configuration with TDOA time-difference measurement error as the optimization target has higher positioning accuracy.

It may be very difficult to receive the signals from satellite positioning systems due to the existing obstacles in indoor environment. Arising from the popularity of smart phones, Wi-Fi based indoor positioning technology has the advantages with convenient deployment and low hardware cost. In this study, we focused on indoor positioning using Wi-Fi fingerprint data that were collected in shopping malls. Due to the volatility of Wi-Fi signals and the high-dimensional sparseness of fingerprint data, we proposed a feature extraction algorithm, called joint multi-task stacked denoising auto-encoder (JMT-SDAE), aiming at reducing the dimensionality of the original fingerprint data and improving the indoor positioning performance in shopping malls. Furthermore, the features extracted by JMT-SDAE and gradient boosting decision tree (GBDT) were merged to construct a hybrid model, named as JMT-SDAE+GBDT. The experimental results based on 13 location datasets showed that the proposed feature fusion model had better positioning accuracy when compared with other existing positioners, and thus confirmed the effectiveness of our proposed feature extraction algorithm through multi-task learning.

Recently, with the rapid development of society, solutions to reduce energy consumption in the world have attracted a lot of attention, especial electric energy. In this regard, a system that can control light on and off by determining the location of the person to reduce the waste of electricity used in public buildings, called intelligent building lighting system. Following the practical requirements of the intelligent building lighting system, a technique for positioning in indoor environments is proposed, supporting the design of a positioning system based on deep learning and the Cerebellar Model Articulation Controller (CMAC), called Y-CMAC.This technique adopts YOLOv3 (the method in the paper of YOLOv3: An Incremental Improvement) for object detections and makes the coordinate of a person in the image. On the other hand, using CMAC to calculate the actual position of the person in the indoor environment. Moreover, massive surveillance video is used to reduce the cost of equipment and facilitate the promotion of applications. The average positioning error is controlled at around 1m in this paper.

Indoor positioning technology is one of the cornerstones for many services in Internet-of-Things (IoT) systems. However, the existing indoor positioning systems are still suffering from several issues, such as unstable positioning accuracy, a high system complexity, and a high deployment cost. To tackle these issues, this article presents a Particle Filter-based Indoor Positioning System (PFIPS) that can localize and track a tag that broadcasts Bluetooth Low Energy (BLE) beacon messages to BLE receivers. The proposed PFIPS uses a Kalman Filter to preprocess collected Received Signal Strength Indication (RSSI) information in order to smooth the fluctuated RSSI data. It also designs an effective Particle Filter (PF) to approximate the location of a tag, which gradually reduces the location uncertainties in a Gaussian belief space. To show the applicability of our PFIPS, we have developed PFIPS in a testbed based on commercial off-the-shelf (COTS) devices. Through intensive simulations and experiments, our experiment results show that our PFIPS outperforms the legacy indoor positioning systems in terms of location accuracy by 24.1% and achieves median accuracy of 1.16 m.

Sufficient training data and high positioning accuracy are crucial components of indoor positioning. However, the collection of learning data consumes much time and manual effort, inhibiting the global spread of indoor positioning technology. The use of crowdsourcing-based data collection that does not require user intervention can reduce deployment effort, but results in loss of positioning accuracy. Pedestrian dead reckoning partly resolves the problem by using a variety of sensors to provide a relatively accurate position; however, the accumulation of errors is yet to be successfully addressed. In this study, we introduce a highly accurate positioning method that implements error correction based on a crowdsourced database. The proposed method constructs learning data without manual effort and the need of reference points in the target area, and improves positioning accuracy by continuously learning and correcting the error distribution of the inertial sensor. The obtained positioning accuracy was approximately 3.38 m for roughly 83% of the collected fingerprints. Furthermore, the error correction algorithm improved the moving distance and direction accuracy by up to 2.86% and 25.7%, respectively. The proposed method was verified through experiments in an office building where it successfully constructed a learning dataset and reflected a dynamic environment by deriving accurate tracking results.

Accurate location information is very important for human beings and agents. However, GPS has very limited positioning accuracy or even can not work in challenging environments such as indoor and urban canyons, and other solutions suffer from different shortcomings. It is very meaningful to use the scene image captured by the high-definition camera on the smart phone to locate indoor through the neural network, but the existing image-based positioning methods do not take into account the interrelationship of diffenrent instances of the same feature space. In this paper we present a novel network architecture, which embeds Scene Enhancement module and Relational Network (RN) into the end-to-end positioning system. The system was tested in three typical scenarios, and the test results show that its performance outerperforms existing image-based schemes in most cases.

In recent years, the rapid development of mobile communication technology has led to the development of positioning services, and the demand for indoor positioning technology has been increasing strictly. In the Internet information era, location-based services (LBS) have been applied in various fields after the emergence of location-based information technology, which brings new experience and convenience to people. In order to improve the indoor positioning accuracy of wireless sensor networks, an indoor positioning algorithm with a rotating arc triangle layout is proposed. This layout uses a circular arc triangle to deploy the beacon nodes and changes the position of the beacon nodes by rotating. Compared with the square, triangle, and equi-arc triangle layout, the rotating arc triangle layout reduces beacon nodes and is easy to rotate, but the layout after rotation has not changed. Experiments show that the layout of the rotating arc triangle layout is simple. By rotating the beacon node, multiple positioning prediction positions are superposed, and the positioning accuracy is improved. The rotating arc triangle layout is better than the square, the traditional triangle, the improved triangle, and the equal arc triangle. The average positioning accuracy of the arc triangle has been increased by 69.9%, 51.8%, 16.6%, 35.6%, and 32.9% respectively, which has certain use value and popularization.

Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial efforts, either in determining the exact location of all beacons to facilitate lateration, or collecting signal strength data from a grid over all locations to facilitate fingerprinting. To reduce this initial setup cost, one may infer the positions using Simultaneous Location and Mapping. In this paper, we use a mobile phone equipped with an Inertial Measurement Unit, a Bluetooth receiver, and an Unscented Kalman Filter to infer beacon positions. Further, we apply adaptive noise modeling in the filter based on the estimated distance of the beacons, in contrast to using a fixed noise estimate which is the common approach. This gives us more granular control of how much impact each signal strength reading has on the position estimates. The adaptive model decreases the beacon positioning errors by 27% and the user positioning errors by 21%. The positioning accuracy is 0.3 m better compared to using known beacon positions with fixed noise, while the effort to setup and maintain the position of each beacon is also substantially reduced. Therefore, adaptive noise modeling of Received Signal Strength is a significant improvement over static noise modeling for indoor positioning.

The smartphone-based Wi-Fi fine time measurement (FTM) technique has provided a new approach for Wi-Fi-based indoor location on mobile phones since the 2018 release of the Google Android Pie system, which supports the IEEE 802.11-2016 protocol and can directly measure the distance between the initiator and the responder. This paper studies in detail the properties of mobile phone Wi-Fi ranging and positioning performance. Considering non-line-of-sight (NLOS) error identification, a real-time ranging error compensation model based on the least-squares (LS) method and an adaptive Wi-Fi FTM positioning algorithm (AWFP) utilizing the weighted least-squares (WLS) method are devised. To improve accuracy, a new tightly coupled fusion positioning algorithm integrating Wi-Fi FTM and built-in mobile phone sensors based on the extended Kalman filter (EKF) is constructed. The experimental results show that the ranging precision and Wi-Fi positioning accuracy are improved. Based on the high-precision Wi-Fi ranging and positioning results, the final location accuracy of the proposed fusion method is 0.98 m, and the root-mean-square error (RMSE) is 1.10 m, which are better than those of the PDR, Wi-Fi FTM and loosely coupled PDR/Wi-Fi FTM integration based on the EKF.

Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.

Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on device attitude. We leverage the STNs to align the device coordinate system and the navigation coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52°, mean heading error of 6.07° and heading error of 9.18° at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications.

This paper presents a novel method of estimating the altitude of pedestrians who are walking in the indoor environment of a multi-story building. We will show that to achieve pedestrian altitude estimation, a pedestrian only needs to wear a small MEMS-based integrated sensing device consisting of a micro-IMU (i.e., consists of a 3D accelerometer, a 3D gyroscope, and a 3D magnetometer) and a barometer, during indoor activities. High-precision estimates of the pedestrian's position in the vertical direction were obtained by utilizing the acceleration and angular rate data, as well as the height, deduced from barometer data. The inherent drifts of the IMU sensors, which lead to cumulative errors in altitude estimation, were sharply reduced using a complementary filter and an error compensation algorithm. The experimental results demonstrate that this method is effective in reducing estimation errors. When a person walks on stairs with the same step height, the error of the estimated height of each step is within 0.5 cm, and the cumulative height error is about 1.7% over a total height of 2.9 m. This integrated sensing device also exhibits good stability, i.e., three 20-min tests in a 12-h period showed that the cumulative error accounts for about 2% of the total height of 11.23 m. When the stairs have different heights, i.e., heights ranging from 12 to 28 cm, the estimated height error of each step is within 2 cm. With an ability to provide accurate and reliable vertical altitude estimates of pedestrians inside a multi-story building, the sensing device developed through this paper is suitable for use in 3D-space body tracking and pedestrian navigation applications.

A UHF radio-frequency identification (RFID) indoor positioning system based on phase difference is presented in this paper. An additional auxiliary tag and the target tag make up a double tag array to eliminate the phase ambiguity. To quantify the phase offset caused by the interference between tags, a mathematical model is built. Based on the double tag array and the interference model, a hyperbolic positioning algorithm, combining the genetic algorithm, is implemented to improve the positioning accuracy. Compared with other RFID positioning systems, the proposed system achieves a better positioning accuracy of 0.2217 m.

Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of

Technologies fusing WiFi-based indoor positioning and pedestrian dead reckoning for indoor target localization have already been proposed for a variety of occasions. Among them, the methods of multisource fusion with particle filter perform well in a random noise environment. However, the positioning accuracy can be significantly affected by the personnel density of the interest area. In this paper, we propose a novel indoor collaborative positioning system which performs dynamic user pairing and adaptive particle-pair filtering with the help of the Chirp acoustic signal distance measurement. The proposed scheme mainly includes two parts in each time slot: first, the users are clustered into cells for dynamic user-pairing, and then the adaptive particle-pair filter iterates the comprehensive weight to obtain the final positioning result. The experiment results show that the proposed scheme can effectively solve the problem of the error increase caused by the intensive personnel, and remarkably improve the indoor positioning accuracy in the crowded public.

Indoor magnetic-based positioning has attracted tremendous interests in recent years due to its pervasiveness and independence from extra infrastructure. Existing methods for indoor magnetic-based positioning are either point-based fingerprint matching or sequence-based fingerprint matching using the raw magnetic field strength. However, the magnetometers in smartphones are vulnerable to a few factors such as user's postures and walking speed, which causes the magnetic field strength corresponding to a location often shift in time or exhibit local distortions, thus greatly limits the positioning performance of existing methods rely on raw magnetic field strength. To this end, we observe the differences among magnetic field strength sequences are mainly attributed to small local segments, and design a new sequence-based fingerprint based on the differences among small local segments of raw MFS sequence to represent raw MFS sequence for indoor positioning. To demonstrate the utility of our proposed sequence-based fingerprint, we have performed a comprehensive experimental evaluation on two datasets, the results show that the proposed approach can significantly improve positioning performance compare with baseline methods.