Wi-Fi Localization Research Articles

WiFi/PDR-integrated indoor localization using unconstrained smartphones

In this paper, we propose a WiFi/pedestrian dead reckoning (PDR)-integrated localization approach based on unscented Kalman filters (UKF). The UKF integrating WiFi localization with PDR is used for ultimate location estimation. Instead of setting process and measurement noise-related parameters empirically as previous works, the error covariance of user heading estimation in PDR state model can be accurately estimated by developing another UKF, while the measurement noise statistics in WiFi localization are estimated by deploying a kernel density estimation-based model. Another developed UKF is used for device attitude tracking in user heading estimation of PDR. Besides, in order to adapt the unconstrained carrying positions and orientations of smartphones, we propose a robust carrying position recognition method based on orientation invariant features. Experimental results show that the proposed WiFi/PDR-integrated localization approach may improve traditional approaches in terms of reliability and localization accuracy.

A Weighted Bayesian Localization Algorithm Based on the Strongest AP Method

This paper presents a kind of using Wi-Fi location fingerprint to locate the interior regions of the weighted Bayesian algorithm based on the strongest AP method. The algorithm locates the regions with the strongest AP signal by voting according to the distribution rule of the online data, and then uses the weighted Bayesian algorithm to locate. The experimental scene is a typical “aisle” interior region, through the data measured and algorithm matching. The results show that compared with the Bayesian algorithm, the weighted Bayesian algorithm based on the strongest AP method has higher positioning precision and accuracy.

A novel radio map construction method with reduced human efforts for Wi-Fi localisation system

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

A novel radio map construction method with reduced human efforts for Wi-Fi localisation system

A novel radio map construction method with reduced human efforts for Wi-Fi localisation system

$M^3$: Multipath Assisted Wi-Fi Localization with a Single Access Point

Owing to the ubiquitous penetration of Wi-Fi in our daily lives, Wi-Fi indoor localization has attracted intensive attentions in the last decade or so. Despite some significant progresses, the high accuracy of existing systems is still achieved at the cost of dense access point (AP) deployment. The more practical single AP localization is largely left as an open problem because the hardware-induced time delay “contaminates” the measurement of signal propagation time in the air. In this article, we design and implement $M^3$ M 3 to tackle this challenge with commodity Wi-Fi cards. $M^3$ M 3 exploits a multipath-assisted approach that turns the harmful multipath from foe to friend to enable single AP localization: a device can be pinpointed through the combination of azimuths and the relative time of flight (ToF) of Line-of-Sight (LoS) signal and reflection signals, eliminating the need for multiple APs along with their absolute ToF measurements. $M^3$ M 3 further utilizes frequency hopping to combine multiple channels to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of $M^3$ M 3 , the channels do not need to be adjacent. Comprehensive experiments demonstrate that $M^3$ M 3 outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP.

Indoor Region Localization With Asynchronous Sensing Data: A Bayesian Probabilistic Model

Obtaining accurate location information of tracked objects is the cornerstone of providing high-quality location-based services (LBSs). Recently, passive localization has become an increasingly important research theme, attracting the attention from both academic and industrial communities. However, existing Wi-Fi fingerprinting-based passive localization methods mainly focus on the indoor localization problem and require Wi-Fi detection equipment to scan wireless communications simultaneously. In this paper, we propose the concept of indoor region localization, which aims at estimating the region where an object stayed for a given time interval. Moreover, a Bayesian probabilistic model is presented to cope with this issue. The key novelty of our method lies in that it allows Wi-Fi detection equipment to behave in an asynchronous fashion, which eliminates synchronization concerns when implementing the positioning system. Specifically, it first introduces a time window for each timestamp, and then aggregates all the sensing records in the given time window to obtain an RSSI measurement vector. Afterward, a kernel function is utilized to measure the conditional probability with respect to every fingerprint, and all these probabilities are combined to obtain the posterior probabilities corresponding to different regions. Extensive experiments are conducted on both public and proprietary data sets and our method significantly outperforms all the comparing algorithms in terms of each evaluation metric.

Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression

WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present Gaussian process regression models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points. As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure.

Li-Fi Positioning for Efficient Millimeter Wave Beamforming Training in Indoor Environment

Enormous data transfer over the Internet demand needs ultra-high-speed data rates and low latency networks, opening the door for 5G and beyond. Immigration towards high frequency bands seems to be an efficient solution due to their large swath of available spectrum. The use of the millimeter wave (mmWave) band and the visible light band, i.e., light-fidelity (Li-Fi), in wireless access are considered as the most promising solutions. Hence, there is an urgent need for efficient interworking among these candidate technologies to increase their usability by overcoming their challenges. In this paper, an efficient Li-Fi/mmWave integration paradigm orchestrated by Wi-Fi signaling is introduced, then a mmWave beamforming training (BT) based on Li-Fi localization is proposed. The wide spread of light emitting diode (LED) bulbs used as Li-Fi atto-cells accompanied with their high accurate positioning enable the use of too sharp (pencil) mmWave beams using a few number of beam switchings. This contributes in highly reducing the mmWave BT complexity while extremely increasing its coverage area. The effectiveness of the proposed mmWave BT, in terms of BT complexity, outage probability and misalignment probability, is proved mathematically and via numerical simulations over the exhaustive search BT and that based on the conventional Wi-Fi localization, especially in sharp beam scenarios (e.g., beamwidth is around 2.8°).

Automatic Detection of Missing Access Points in Indoor Positioning System †.

The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks,k-nearest neighbors,K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile

The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.

Robust Wi-Fi fingerprinting-based positioning in the presence of lying identities

The lying identity of an access point (AP) is one of the most serious threat in Wi-Fi positioning because an adversary can easily acquire a valid address by monitoring the transmission and masquerade as another AP in the networks. This study proposes a robust Wi-Fi localization algorithm that can tolerate the liars instead of explicitly detecting them. The proposed algorithm considers all possible combinations of APs in an unionbased approach such that the adversaries cannot easily affect the positioning results by masquerading APs. Onsite experimental results demonstrate that this approach apparently achieves more robust location estimation than the Bayesian approach and the cluster-based method in the presence of lying identities.

An adapted surrogate kernel for classification under covariate shift

A general solution to covariate shift is to minimize a distributional discrepancy between training and test data. To solve this problem, kernel-based learning methods minimize the discrepancy in a kernel-induced feature space by transforming training data to be similar to test data and learn the transformed data in the feature space. However, when they are applied to classification tasks, transformed training data points from different classes can be mixed up in the kernel-induced space since they ignore the class labels in training data. Therefore, we propose an adapted surrogate kernel that is able to manage the large class discrepancy. The proposed method incorporates the class discrepancy into the surrogate kernel which tries to minimize the discrepancy in a kernel-induced feature space. To do this, we interpret the surrogate kernel with the Nyström method which allows prior knowledge to be incorporated into kernel approximation. By using class discrepancy derived from training data as prior knowledge, the adapted surrogate kernel does not keep only the role of surrogate kernel, but also the large class discrepancy among classes. The experimental results on several classification tasks including text classification and WiFi localization prove that the proposed kernel results in significant improvement in classification performance under covariate shift.

SiFi

There has been a booming interest in developing WiFi localization using multi-antenna (MIMO) access points (APs). Recent advances have demonstrated promising results that break the meter-accuracy barrier using commodity APs. Yet these state-of-the-art solutions require either multiple APs that are not necessarily available in practice, or multiple-channel measurements that disrupt normal data communication. In this paper, we present SiFi, a single AP-based indoor localization system that for the first time achieves sub-meter accuracy with a single channel only. The SiFi design is based on a key observation: with MIMO, the multiple (typically three) antennas of an AP are frequency-locked; although the accurate Time-of-Arrival (ToA) estimation on commodity APs is fundamentally limited by the imperfect time and frequency synchronization between the transmitter and receiver, there should be only one value for the ToA distortion that can cause three direct-path ToAs of the antennas to intersect at a single point, i.e., the position of the target. We develop the theoretical foundations of SiFi and demonstrate its realworld implementation with off-the-shelf WiFi cards. Our implementation introduces no hardware modification and is fully compatible with concurrent data transmission. It achieves a median accuracy of 0.93 m, which significantly outperforms the best known single AP single channel solution.

Simulation of Fusion Localization Based on a Single WiFi AP and PDR

WiFi Localization and Pedestrian Dead Reckoning(PDR) are attracting increasing attention due to its wide deployment and ubiquity on smartphones respectively. In this paper, we propose a fusion system based on Kalman Filter(KF) algorithm which combines a Single WiFi AP positioning system and PDR. Single WiFi AP positions depends on ToF calculation and AoA calculation which are breakthroughs in recent researches. PDR is variant of dead reckoning system which rely on inertial sensors on mobile phones. The fusion system makes best use of the advantages and bypass the disadvantages. Simulation results demonstrate that the performance of the fusion system outperforms the two independent systems.

The Locator Framework for Detecting Movement Indoors

There are many advantages to being able to track in real-time the movement of things or humans. This is especially important in tracking goods in the supply chain, in security and also in health and safety. The Global Positioning Satellite (GPS) system works well in outdoor environments but it cannot track items indoors. There is also the problem of power hungry sensor chips inherent in some GPS trackers. Mobile Cellular triangulation also works well for many outdoor solutions but problems with cost, accuracy and reliability make it difficult to deploy for indoor tracking scenarions. The levels of accuracy can vary by up to 50 meters which hinder its ability for adoption in many use case scenarios. There are also problems with poor cellular coverage in rural areas. Solutions built on WiFi–the IEEE 802.11 standard overcome many of these issues. WiFi location tracking works via sampling of the received signal strength (RSS) which along with triangulation and prior mapping allows systems to locate items or humans with fine-granularity. This WiFi fingerprinting is a viable cost-effective approach to determining movement within indoor enviroments. This paper presents an overview of popular techniques and off-the-shelf solutions which can be used to determine movement of people and objects indoors. We outline the Locator frameworks which is built on both active and passive indoor localisation techniques for tracking movement within indoor environments.

A localization and tracking scheme for target gangs based on big data of Wi-Fi locations

The modeling and analysis of target gangs’ usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs’ usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs’ activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.

Infinite-Term Memory Classifier for Wi-Fi Localization Based on Dynamic Wi-Fi Simulator

Wi-Fi localization is an active research topic, and various challenges are not yet resolved in this field. Researchers develop models and use benchmark datasets for Wi-Fi or fingerprinting to create a quantitative comparative evaluation. These benchmarking datasets are limited by their failure to support dynamical navigation. As a result, Wi-Fi models are only evaluated as usual classifiers without including actual navigation maneuvers in the evaluation, which makes the models incapable of handling the actual navigation behavior and its impact on the performance. One common navigation behavior is the cyclic dynamic behavior, which occurs frequently in the indoor environment when a person visits the same place or location multiple times or repeats the same trajectory or similar one more than once. For this purpose, we developed two models: a simulation model for generating time series data to support actual conducted navigation scenarios and a Wi-Fi classification model to handle dynamical scenarios generated by the simulator under cyclic dynamic behavior. Various testing scenarios were conducted for evaluation, and a comparison with benchmarks was performed. Results show the superiority of our developed model which is infinite-term memory online sequential extreme learning machine (OSELM) to the benchmarks with a percentage of 173% over feature adaptive OSELM and 1638% over OSELM.

Local System of Positioning Using a Wifi Network

The WiFi technology is widespread. To launch the WiFi network is very simply, thus, they meet so often. Knowledge of the location of the antennas Wi-fi is very important for internal localization (position fix of an object). System of positioning of Wi-fi (WPS) or WiPS/WFPS are used where GPS and GLONASS are unavailable because of different reasons including multipath and signal blocking indoors. Such systems include as well internal systems of positioning. Use of Wi-fi not for creation of a network, and (for example, positioning of devices on the WiFi network) is urgent for other purposes because there is a rapid growth at the beginning of the 21st century of wireless access points in urban areas. This article discusses practical application of a method of localization with use of WiFi of access points which is based on measurement of intensity of the received signal (indication of the received signal or RSSI). Here the device which executes the measurements necessary for system operation is described. Some formulas for computation of layout of the device are given. Results of system operation are considered with different layouts of access points in the given space and different environments.