Wi-Fi Indoor Positioning System Research Articles

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories.

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

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.

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System.

A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc fine-timing measurement (FTM) scheme also known as the Wi-Fi round trip time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of the following: 1) systematic bias determination and removal; 2) clustering-based trilateration (CbT) supported by weighted concentric circle generation (WCCG), namely CbT & WCCG; 3) positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & WCCG methods are compared with least square (LS) and recursive least square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer–Rao bound (CRB).

Modeling and predicting the occupancy in a China hub airport terminal using Wi-Fi data

The research on building energy consumption of airport terminal is of great importance, especially considering the rapid development of civil aviation in China. One of the key elements influencing the building energy consumption is the occupancy. This paper proposes an occupancy model based on dwell time distribution and applies it to the data set acquired through the Wi-Fi indoor positioning system. Firstly, this paper briefly describes the passenger flow in a Chinese hub airport terminal in 66 days. On this basis, the distribution of passengers’ dwell time, averaging at 96.97 min but varies with time, airlines or delay, is extracted and modeled through a Bayesian method. Furthermore, the occupancy is effectively predicted in high spatial-temporal resolution based on the flight schedule and the model before, reaching a 0.747 r-square. The new data source and mathematical model will be instructive in future occupancy research, and the prediction model will benefit the optimization of the operational strategy of terminal buildings.

Design of WIFI Indoor Positioning System Based on a Combination of Fingerprint Identification Algorithm

The popularity of WiFi hotspots provides convenient environmental conditions for WiFi indoor location, making WiFi indoor location with high commercial value. In order to improve the quality of indoor positioning system and improve the accuracy and efficiency of indoor positioning, this system designs a WiFi indoor positioning system based on a combination of fingerprint identification on Android platform. Experimental results show that this WiFi indoor positioning system can be more accurate for indoor positioning.

A novel WiFi indoor positioning strategy based on weighted squared Euclidean distance and local principal gradient direction

PurposeThis paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.Design/methodology/approachThe local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.FindingsExperiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.Research limitations/implicationsWeighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.Practical implicationsThe new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.Social implicationsThe received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.Originality/valueA novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

Automated Construction and Maintenance of Wi-Fi Radio Maps for Crowdsourcing-Based Indoor Positioning Systems

If the construction and maintenance of Wi-Fi radio maps (WRMs) were fully automated, the implementation of a global-scale Wi-Fi indoor positioning system would be possible. This paper proposes a WRMs calibration system that automates the initial construction and maintenance of radio maps using crowdsourced fingerprints collected from numerous smartphones without location information. The system incorporates an unsupervised learning algorithm into an incremental and adaptive calibration process. The unsupervised learning algorithm constructs an initial radio map, using fingerprints collected from unknown locations, by finding a hidden structure among them. Once a positioning service is available based on the initial radio map, the radio map continues to adapt to signal changes in the environment through the incremental and adaptive calibration process using the fingerprints that are continuously collected from the service users. Experiments carried out in an office building have shown that the proposed system could successfully construct and maintain a precise radio map without requiring any location information. In a long-term experiment that lasted for five months, the proposed system was able to not just maintain but also improve the quality of the radio map. These results indicate that Wi-Fi indoor positioning systems can be automatically constructed and maintained in continuously changing Wi-Fi environments without manual calibration efforts.

A new perspective of environmental behaviour study: a brief introduction of Wi-Fi indoor positioning system

Environment behaviour study is fundamental to architectural and urban design. The traditional methods have difficulty in obtaining quantitative data on a large-scale, which indirectly hinders the intelligent process of building environment. The newly developed Wi-Fi indoor positioning system can provide a large number of effective data to environment behaviour study. This study gives a brief introduction to the Wi-Fi positioning system and analyses the abundant information contained in its data. On this basis, this study takes an innovation incubator as an example, trying to explore human interaction in the environment with the tool of social network analysis. Finally, this study provides a simple framework for implementing smart home with positioning data. The conclusions highlight the advantages and limitation of Wi-Fi positioning system, as well as the promising prospect in the smart home.

A new perspective of environmental behaviour study: a brief introduction of Wi-Fi indoor positioning system

A new perspective of environmental behaviour study: a brief introduction of Wi-Fi indoor positioning system

Real Time Wi-Fi Indoor Positioning System Based on RSSI Measurements: A Distributed Load Approach with the Fusion of Three Positioning Algorithms

This paper investigates the effectiveness and applicability of fusing three wireless positioning algorithms to determine the positions and track nomadic sensor nodes in real environment conditions. We fuse finger printing and atomic multilateration processes to give the system the best feasible region and to ensure that the later does not sway much due to accumulative errors. The extended Kalman filter is then used for refining the estimated position in near real time. The paper further assesses the response speed and the accuracy of estimating the position of the nomadic nodes with a prudent distribution of the processing load.

Radius based domain clustering for WiFi indoor positioning

PurposeNowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.Design/methodology/approachThe proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.FindingsExperiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.Research limitations/implicationsWeighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.Practical implicationsThe RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.Social implicationsThe RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.Originality/valueA novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

Flexible indoor localization and tracking system based on mobile phone

As the WIFI access points are widely deployed, the received WIFI signal strength is commonly adopted as a positioning characteristic for mobile phone based indoor localization systems. Although WIFI based localization has achieved great development, there are still several key challenges in tracking applications, such as how to modify irregular trajectory obtained from the sequential positioning results. To tackle those challenges, this paper integrates the typical WIFI indoor positioning system with a Pedestrian Dead Reckoning (PDR) system based on the sensors in the mobile phone as many newly emerged systems proposed. The Maximum Likelihood (ML) algorithm is proposed to retrieve the user's initial location and moving direction without any intervention from the user. During the tracking process, a filtering algorithm can revise the moving direction indicated by the sensors only if a straight walking is detected. To obtain more accuracy and efficiency, a combination of Kalman Filter (KF) and auto-adaptive dynamic grid filter (GF) named KAGF is proposed for the fusion of the results from WIFI and PDR system. Experiments in the real scenarios show that our fusion system achieves better results than the widely adopted one, in which the particle filter is used, both in accuracy and computational complexity. Furthermore, the system's effectiveness is improved largely with longer WIFI updating period and larger reference points’ interval to achieve the same encouraging results.

Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System.

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.

Intelligent Fusion of Wi-Fi and Inertial Sensor-Based Positioning Systems for Indoor Pedestrian Navigation

Indoor positioning systems based on wireless local area networks are growing rapidly in importance and gaining commercial interest. Pedestrian dead reckoning (PDR) systems, which rely on inertial sensors, such as accelerometers, gyroscopes, or even magnetometers to estimate users' movement, have also been widely adopted for real-time indoor pedestrian location tracking. Since both kinds of systems have their own advantages and disadvantages, a maximum likelihood-based fusion algorithm that integrates a typical Wi-Fi indoor positioning system with a PDR system is proposed in this paper. The strength of the PDR system should eliminate the weakness of the Wi-Fi positioning system and vice versa. The intelligent fusion algorithm can retrieve the initial user location and moving direction information without requiring any user intervention. Experimental results show that the proposed positioning system has better positioning accuracy than the PDR system or Wi-Fi positioning system alone.

Performance and accuracy test of a WiFi indoor positioning system

Location based-services and personal navigation require the location determination of a user not only in outdoor environments but also indoor. For indoor location already established wireless infrastructure such as WiFi (Wireless Fidelity) or WLAN (Wireless Local Area Networks) can be employed. This approach has the advantage that no costly hardware installations are necessary inside a building if WiFi is already available. The IMST GmbH has developed a software framework called "ipos" for indoor positioning technologies. The principle of the developed platform and the performance of the location determination using WiFi in a localization testbed of IMST GmbH are presented in this paper.