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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 M3 to tackle this challenge with commodity Wi-Fi cards. M3 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. M3 further utilizes frequency hopping to combine multiple channels to form a virtually wider-spectrum channel for higher ToF resolution. As a prominent feature of M3, the channels do not need to be adjacent. Comprehensive experiments demonstrate that M3 outperforms the state-of-the-art systems and achieves a median localization accuracy of 71 cm in three environments with a single AP. [ABSTRACT FROM AUTHOR]

As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains more position related information than traditional received signal strength, is organized into a time-frequency matrix that resembles image and utilized as the feature for localization. The ConFi models localization as a classification problem and addresses it with a five layer CNN that consists of three convolutional layers and two fully connected layers. The ConFi has a training stage and a localization stage. In the training stage, the CSI is collected at a number of reference points (RPs) and used to train the CNN via stochastic gradient descent algorithm. In the localization stage, the CSI of the target device is fed to the CNN and the localization result is calculated as the weighted centroid of the RPs with high output value. Extensive experiments are conducted to select appropriate parameters for the CNN and demonstrate the superior performance of the ConFi over existing methods.

Abstract This paper presents a tracking framework for enhancing the positioning accuracy of a mobile device by fusing the positions provided by a GPS navigation system and those obtained using Wi-Fi signal strength measurements, in urban environments. To achieve an efficient fusion, a structure based on two particle filters and a Multiple Model (MM) approach is proposed. It fuses the information coming from these two independent technologies, to overcome their own drawbacks. Indeed, the Wi-Fi and GPS are viewed as two models, whose probabilities are calculated using a Transition Probability Matrix (TPM) and a Mixing Likelihood Function (MLF). These probabilities are then used to combine the mobile state estimates, provided by the two particle filters. Matched to the two models, these filters interact by exchanging a part of their particles. The proposed architecture is experimentally evaluated and compared with the pure Wi-Fi and GPS positioning systems and other fusion methods. The results indicate that the positioning errors of the proposed scheme are the lowest. [ABSTRACT FROM AUTHOR]

A RSS-based infrastructure-free localization algorithm was proposed,which is only based on Wi-Fi signals and does not require any additional infrastructure.It reduces the database construction cost by a special dynamic method.By picking the hot spot,it can effectively alleviate the interference from other wireless signals in the nearby region.It also enhances the RSS–based matching algorithm and hence improves the localization accuracy.The algorithm is easy to operate and doesn’t need any complex participation from end users.The algorithm is suitable for those who can provide positioning service in some special locations.Through the deployment of dedicated hot spots,it will reduce the environment interference and improve the quality of location-based service.

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding.

We investigate the use of linear adaptation when fusing Wi-Fi maps built from spatially sparse received signal strength measurements obtained with multiple devices. First, we show that the residual of the linear regression between devices, usually unaccounted for in existing cross-device localization work, is an important indicator of device dissimilarity and a good predictor of localization performance. Through explicitly modeling the device dissimilarity, one can improve localization accuracy when fusing training sets from multiple devices by weighting each training set differently. Second, we use the Gaussian process (GP) sensor model to develop a regression algorithm which more reliably estimates the linear fit and device dissimilarity given only a few labeled samples from each new device. By accounting for device dissimilarities in map fusion and by using the proposed regression algorithm, localization performance can be greatly improved given just a few training samples from a new device. Also, when fusing multiple existing maps for a new device using regression misfit, performance is improved by 3.5 to 10 percent. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for indoor localization, developed with the main aim of making it useful for real-world deployments. Many indoor localization methods exist, yet they have several disadvantages in real-world deployments--some are static, which is not suitable for long-term usage; some require costly human recalibration procedures; and others require special hardware such as Wi-Fi anchors and transponders. Our method is self-calibrating and self-adaptive thus maintenance free and based on Wi-Fi only. We have employed two well-known propagation models--free space path loss and ITU models--which we have extended with additional parameters for better propagation simulation. Our self-calibrating procedure utilizes one propagation model to infer parameters of the space and the other to simulate the propagation of the signal without requiring any additional hardware beside Wi-Fi access points, which is suitable for real-world usage. Our method is also one of the few model-based Wi-Fi only self-adaptive approaches that do not require the mobile terminal to be in the access-point mode. The only input requirements of the method are Wi-Fi access point positions, and positions and properties of the walls. Our method has been evaluated in single- and multi-room environments, with measured mean error of 2-3 and 3-4 m, respectively, which is similar to existing methods. The evaluation has proven that usable localization accuracy can be achieved in real-world environments solely by the proposedWi-Fi method that relies on simple hardware and software requirements. [ABSTRACT FROM AUTHOR]

Highlights: [•] The entropy is employed as a new metric to evaluate the fingerprint-based Wi-Fi location accuracy. [•] The relations of entropy and accuracy with variable RP densities and standard deviations are given. [•] The reckless increase of the number of APs cannot be an effective way to improve location accuracy. [•] The substantial saving of computation cost by the entropy is achieved over the existing metrics. [ABSTRACT FROM AUTHOR]

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half. [ABSTRACT FROM AUTHOR]

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients' wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients' requirements for wayfinding. [ABSTRACT FROM AUTHOR]