Wi-Fi Positioning Research Articles

Mobility-Induced Graph Learning for WiFi Positioning

Mobility-Induced Graph Learning for WiFi Positioning

Autonomous wireless positioning system using crowdsourced Wi-Fi fingerprinting and self-detected FTM stations

Wi-Fi positioning system (WPS) has been proven as an effective way to realize universal indoor navigation for smart city-related applications. The localization ability of the existing WPS is affected by the low precision of the crowdsourced database and the unknown location of local wireless stations. This paper develops an integrated indoor localization framework using crowdsourced Wi-Fi fingerprinting and self-detected Wi-Fi Fine Time Measurement (FTM) stations (IL-CFSW). A novel crowdsourced mobile sensors data modeling algorithm with self-calibrated parameters is proposed, and modeled trajectories are further segmented and matched with the existing pedestrian indoor network to enhance the performance of the final database generation. Furthermore, the iteration unscented Kalman filter (iUKF) is adopted to recognize the position and calibrate the bias of existing Wi-Fi FTM anchors combined with the hybrid distance measurement model. Finally, an enhanced particle filter with error ellipse constraint is developed to fuse different location sources and indoor network information. Comprehensive experimental results present that the developed IL-CFSW can achieve autonomous 3D indoor localization performance in multi-floor contained scenes and meter-level positioning accuracy is achieved with the consideration of pedestrian indoor network information.

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client’s muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that “sub-cm level” navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning

Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning

Accuracy enhancement of Wi-Fi positioning by ranging in dense UWB networks

This study proposed a fusion-based approach of Wi-Fi fingerprint positioning with relatively accurate UWB ranging in dense wireless networks (DWNs) to optimize indoor localization accuracy. Such an optimization was achieved through a proposed objective or “loss” function that minimizes the total positioning errors of the nodes composing a virtual geometric structure with the internode distances preserved during optimization in the DWN. Theoretical analysis and simulation results of DWNs up to 100 nodes revealed that, with at least 6 extra measured distances among as few as 4 participating nodes including the device-under-targeting (DUT) itself, the resulting localization accuracy improvement was proportional to the square root of the number of participating nodes towards the limit of the ranging error. Per experiment outcomes of a 10-node UWB DWN, our Wi-Fi/UWB ensemble reduced the baseline Wi-Fi K-nearest neighbors (KNN) fingerprint localization error by up to 69.8 %, further validating our theoretical and simulation results.

5G Vs Wi-Fi Indoor Positioning: A Comparative Study

Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.

Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

As the essential infrastructure and important transportation hubs, airport terminals play a strategic role in sustainable cities and urbanization. Due to the strict service requirement and round-the-clock operation, the high energy consumption poses unprecedented challenges to developing environment-friendly airport terminals. Actually, passenger flow as a root cause decides the startup and operation of service facilities including air-conditioning systems. But due to spatial connectivity, parameter coupling, and passenger mobility, resource allocation and system tuning have not been effectively related to passenger travel. In this work, Guangzhou Baiyun International Airport Terminal 2 was taken as an object, and a passenger spatiotemporal distribution model was developed and its contribution to fresh air energy saving was estimated. Among them, zone partition and expression rules were formulated based on insect intelligent building (I2B) architecture to construct Wi-Fi positioning system; Next, for domestic and international passenger, their arrival patterns were fitted by Chi-Square distribution with R2 > 0.993, and their spatiotemporal distribution was predicted with R2 over 0.74 and 0.67 respectively; Lastly, a hybrid reset strategy for fresh air volume set-point combing fuzzy and linear rules was proposed with results showing that HVAC systems running on variable occupancy would lead to considerable energy savings.

Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern Analysis

The recent advances in smart building technologies have enabled us to collect massive Wi-Fi network based trajectory data, which provide an unparalleled opportunity for understanding the indoor user mobility pattern and enabling a wide range of business applications. While some previous studies have explored the Wi-Fi positioning of users, there still lacks a systematic and effective solution for indoor user mobility pattern analysis based on Wi-Fi trajectory data. To this end, in this paper, we propose a unified framework for modeling Wi-Fi trajectory data, namely HWTE, which can empower various tasks of indoor user mobility pattern analysis, such as user classification, next location prediction and schedule estimation. Specifically, we first propose a session trajectory construction module to extract the spatio-temporal semantic information from the Wi-Fi trajectories of users. Then, we devise a pre-training module to learn the unified representation of Wi-Fi trajectories. In particular, a session position embedding technique and a position query task is introduced to enhance the representation ability of the whole trajectory. Moreover, we further propose a hierarchical Transformer-based fine-tuning module to support various application tasks with time and space efficiency. Finally, we validate our framework on a real-world dataset with all three kinds of downstream tasks.

Enhanced propagation model constrained RSS fingerprints patching with map assistance for Wi-Fi positioning

Wi-Fi fingerprint-based positioning is widely used caused by its low cost and ease of implementation when Global Positioning System technology struggles to obtain signal and accuracy in indoor environments. A prerequisite for Wi-Fi fingerprint-based positioning is the availability of an accurate fingerprint database. However, in practice, access to a large proportion of the area is restricted, leading to a rapid degradation of the positioning accuracy of existing interpolation techniques in the absence of reference data in the vicinity of the area. In this paper, we propose an enhanced propagation model constrained fingerprint patching algorithm for incomplete RSS fingerprint databases, aiming to extend the coverage of fingerprints and reduce the overall workload of survey. The algorithm integrates a map-assisted wireless signal propagation models with incomplete spatially sampled fingerprint data to construct a propagation model for each Wi-Fi access point (AP). Recalculate the received signal strength at each reference point for each AP in the fingerprint database according to the specific model. The fingerprint information for the unrecorded regions is then refined by interpolation and the fingerprints initially collected are optimized. We experimentally validate the effectiveness of the proposed interpolation algorithm and compare it with the conventional Support Vector Regression (SVR), Gaussian Process Regression (GPR), and rational quadratic kernel (RQK) methods. The results show that the positioning performance of proposed method is improved by 18.1%, 23.1% and 39.7% in the blank area relative to RQK, GPR and SVR, respectively. In addition, our method outperforms other algorithms with stable performance in different scenarios, especially in scenarios containing obstacle walls, and the performance benefit appears more notable in the complicated indoor scenario with cramped rooms.

Developing three dimensional localization system using deep learning and pre-trained architectures for IEEE 802.11 Wi-Fi

The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

Sensor-Aided Learning for Wi-Fi Positioning With Beacon Channel State Information

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform.

Graph Optimization Model Fusing BLE Ranging with Wi-Fi Fingerprint for Indoor Positioning.

To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the improvement in positioning can be formulated as a nonlinear least-squares optimization problem that a graph can represent. The graph regards users as nodes and our self-designed error functions between users as edges. In the graph, the nodes obtain the initial coordinates through Wi-Fi fingerprint positioning, and all error functions aggregate to a total error function to be solved. To improve the solution effect of the total error function and weaken the influence of measurement error, an information matrix, an edge selection principle, and a Huber kernel function are introduced. The Levenberg–Marquardt (LM) algorithm is used to solve the total error function and the affine transformation estimation is used for the drifting solution. Through experiments, the influence of the threshold in the Huber kernel function is explored, the relationship between the number of nodes in the graph and the optimization effect is analyzed, and the impact of the distribution of nodes is researched. The experimental results show improvements in the positioning accuracy of four common Wi-Fi fingerprint-matching algorithms: KNN, WKNN, GK, and Stg.

Geomagnetic Positioning-Aided Wi-Fi FTM Localization Algorithm for NLOS Environments

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

DTTV Localization with Fingerprinting Technique and Clean Algorithm Based on Measurement Data

Digital terrestrial television (DTTV) technology has been developed and used to broadcast the television program. A numerous applications have been developed for the additional feature used with DTTV. One of these features that can be used which is the localization system with DTTV broadcasting. The advantage of DVB-T2 broadcasting channel for localization technology which very wide coverage area that covered whether outdoor and indoor environment. In the present there are divers methodologies to locate the position of an object or user such as the Global Positioning System (GPS), Cellular Positioning System (CPS) and Wi-Fi Positioning System (WPS). Nowadays there are various application that used for monitoring and controlling such as a water level sensor system, a traffic control system, an intrusion monitoring system etc. that consists of the localization system. The received data can’t be useful without the accuracy location. The mentioned foregoing system still have a limitation in some environment such as the GPS signal is not accessible to some environments, the CPS signal is based on a cell phone tower and the WPS is based on Wi-Fi hotspot. Therefore, the accuracy of localization is decreased. In order to overcome the foregoing limitation of these three systems the complementary remedy the poor coverage is required. The objective of this research is to improve the DVB-T2 propagation channel by a Clean algorithm to eliminate the noise propagation channel for an accuracy of localization system. This technique is very useful for localization analysis in DTTV technology. The distinctive advantage of the DTTV localization is the wide coverage of signal whether an indoor or outdoor environment. Moreover, when the Clean algorithm has been used the noise in propagation channel has been eliminated lead to the accuracy of location receive.

Positioning for Search and Rescue in GPS-Denied Area by Distributed WiFi RSS-Based DoA Modules

Environmental factors affect the Received Signal Strength (RSS) so the received data exhibit random propagation characteristics, such as fading and shadowing effects. This study proposes a Direction of Arrival (DoA) module for the Search and Rescue (SaR) or anti-drone applications, which uses patch antennas and a rat-race coupler as the receivers. An angular estimation is presented for WiFi positioning, using optimally placed DoA antennas and a pseudo-inverse algorithm. The experiment is performed in a university campus with the device under test (DUT) as the transmitter. Using four DoA antennas as receivers at the corners of a 100m×100m region, the mean positioning deviation is about 2m, which is 2% of the region. The confidence range for the estimated positions is also calculated.

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This study introduces a dataset that includes the Wi-Fi received signal strength (RSS) gathered using four different devices, namely Samsung Galaxy S8, S9, A8, LG G6, and LG G7, operated by three surveyors, including a female and two males. In addition, three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment. Various levels of human mobility have been considered in dynamic environments. To analyze the time-related impact on Wi-Fi RSS, data over 3 years have been considered.

WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.

Improving Room-Level Location for Indoor Trajectory Tracking with Low IPS Accuracy

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector improves recognition capability of the uncertain signal and noise, while the signal distance corrector improves the robustness and fault tolerance of the highly variable Wi-Fi signals. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots. The results show that the proposed integrated model successfully provides reliable position estimation when the access points are lost under cybersecurity threats.

Moving Target Localization in Sports Image Sequence Based on Optimized Particle Filter Hybrid Tracking Algorithm

This paper proposes a fusion stadium positioning algorithm, which uses self-optimizing particle filter to integrate the improved athlete dead reckoning and WiFi position fingerprint algorithm for stadium positioning. In order to determine the initial absolute position of the stadium positioning, for athletes entering the stadium from the outside, a seamless switching algorithm outside the stadium is proposed, using the characteristics of high-altitude satellite GPS to find a suitable switching point as the initial absolute position. If in the stadium, WiFi static positioning determines the initial absolute position. Then, aiming at the problem that the poorly diversified particles cannot be better integrated and localized, a self-optimized particle filter algorithm is proposed. After resampling and retaining high-weight particles, the characteristics of low-weight particles are embedded in the copied high-weight particles. This can improve diversity, and we finally carry out fusion positioning. The target tracking algorithm based on Mean Shift has a fixed-scale tracking window, and the tracking effect of variable-size targets is not ideal. In this paper, an affine transformation algorithm is introduced to improve it. First, we iterate the adjacent image frames in reverse Mean Shift to determine the center position of the target and then use the corner matching method to perform template matching on the target to adjust the size of the tracking window. Through simulation verification, it is proved that the optimized particle filter hybrid tracking algorithm can achieve the ideal result when the target size changes. For the image sequence S1, the tracking window of the 20th frame and the 40th frame has a small offset, but the optimal position can be quickly found by Mean Shift iteration. For the image sequence S2, between the 40th frame and the 60th frame, the target occlusion causes the accuracy of the target template to decrease, and the Bhattacharyya coefficient is at a relatively low value. For the image sequence S3, the tracking effect of the optimized particle filter hybrid tracking algorithm meets the requirements.