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Indoor Localization Method Based on WiFi Signals and Building Layout Model

Indoor Localization Method Based on WiFi Signals and Building Layout Model

Recently, the solutions that fuse data from inertial measurement units (IMU) and the WiFi localization systems became a popular topic in the research of indoor localiza- tion. IMUs have become affordable and therefore the manufacturers of mobile devices (e.g. phones, tablets, etc.) have started to implement them, which in term increased the interest of the research community into exploring their potential. A typical IMU consists of a multi-axis accelerometer, a gyroscope and a magnetometer. For example, the work, done by Alvarez, Alonso and Trivino [  ], proposes the activity recognition system that uses WiFi-based location system, combined with accelerometers for the body posture recognition. Their WiFi localization system is using a fuzzy-rule-based classifier that was generated on a training set obtained by fingerprinting the environ- ment. Deng [  ] exploits the fusion of IMU data and the WiFi location service that is based on fingerprinting, by using the extended Kalman filter in the data fusion pro- cess. As we would like to define the localization approach that would suite the future of IoT, this is not the best solution. The majority of proposed IoT devices are not handheld and therefore we cannot acquire useful information from the IMU sensors. The works done by Wu, et al. [  ,  ] show that the channel state information (CSI) can be more accurate and stable than RSS. Their work emphasizes the problem of defining the path loss (fading) exponent and other environmental parameters. Their research defines the path loss exponent in a per-AP manner. It can be seen that the definition of variables in the propagation model is one of the greatest difficulties of the modeled approaches. We are convinced that the model used in the real environment should adapt these variables to the changes in the setting, as people and other present WiFi devices all have an influence on the wireless signal propagation, due to inter- ferences, scattering, reflections, etc. Thus, our research is devoted to defining these values in an online manner and without human intervention. Regarding the utiliza- tion of information about WiFi, other than RSS (e.g. CSI, SNR), we are devoted to developing the methods which can be used on every simple WiFi-connected device; in some devices, information other than indication of RSS is difficult to obtain – e.g. Android, which is one of the biggest mobile platforms, gives only information about RSS through the provided API [  ].
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Simulation of Fusion Localization Based on a Single WiFi AP and PDR

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

Pedestrian Dead Reckoning(PDR) are based on step frequency detection and step length estimation. The positioning error of PDR won’t converge if there isn’t any other assistance due to accumulative localization error resulting from heading and step length estimation errors. In order to overcome error accumulation and improve positioning accuracy and stability, traditional methods usually base on integration of RSSI attenuation model or fingerprint matching positioning results with over 3 WiFi hotspots and PDR system. In this paper, we proposed a system based on fusion method. By combination of single WiFi AP positioning(SWAPP) and pedestrian dead reckoning(PDR), we overcame poor accuracy of single WiFi positioning and cumulative error and unknown initial position of PDR systems. This paper proposes a fusion method based on Kalman Filter(KF) for the single WiFi AP and PDR system to restrain error of PDR system. The fused system improves the accuracy and stability of the pedestrian tracking calculation indoor positioning.
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WiFi Localization and Navigation for Autonomous Indoor Mobile Robots

WiFi Localization and Navigation for Autonomous Indoor Mobile Robots

Abstract— Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs.
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Spatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization

Spatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization

RSSI map contains the measurements of received access point (AP) signal strengths at different locations, which are used to estimate the present location of a received RSSI. Sta- tistical metrics, such as the average, variance, signal coverage, etc, are calculated from RSSI fingerprints database and stored in the RSSI map for further matching. However, the RSSI map training phase takes large amounts of labor and time, both for RSSI sampling and for computing. Many researchers have discussed constructing efficient RSSI maps, both for saving costs and improving localization accuracy. For example, [4] deployed the color radiomap interpolation methods, and [5], [6] periodically updated the database with auxiliary automatic equipment. [7] proposed a compressive sensing method for constructing the RSSI map with a small set of sampling since WIFI signals are sparse in DCT domain. [8] achieved pre- classification of the RSSI database by support vector machine (SVM) based training. And [9] observed the RSS distribu- tion has two peaks, and utilized the double-peak Gaussian distribution metrics for RSSI database construction. Yet few researchers focused on the S-T correlation of collected RSSIs, which has the potential of filling up the unsampled locations based on the S-T similarity of neighbors, thus reducing the sampling density. In this paper we propose an S-T similarity model in the training phase, to create a fine-grained, up-to-date RSSI map.
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Map-assisted Indoor Positioning Utilizing Ubiquitous WiFi Signals

Map-assisted Indoor Positioning Utilizing Ubiquitous WiFi Signals

In this work, the RSS attenuation caused by walls are considered and formulated, which is depicted as Eq. (3.16). The effectiveness of it and the coefficient parameter ω indicating the attenuation factor per wall thickness unit are investigated. The number of walls and the signal’s angle of arrival relative to the walls between AP and RP are obtained from the proposed indoor map system using the wall detection algorithm discussed in Section 3.4.1. The RSS attenuation calculation formula takes over the information of walls. In our work the thickness of walls of our experimental building is considered to be the same, i.e., wall thickness unit t i of the i th wall is constant 1. The attenuation factor per wall thickness is determined based comparison with real-world measurements. The Fig. 3.10 shows the RSS predicted by the log-distance path loss model that considers the attenuation of walls (Eq. (3.11)), where different attenuation factor per wall thickness is applied. In Fig. 3.10 only the area where the RSS is above -90 dBm is shown on the indoor map. The increase of attenuation factor causes the decrease of RSS dramatically and reduces the coverage area of the AP. The difference between observed and predicted RSS using different attenuation factor is compared in all the locations where the RSS are observed, which is evaluated by the cumulative distribution function (CDF), as illustrated in the Fig. 3.11. Setting the attenuation factor to extreme small (i.e., ω = 2) or extreme large (i.e., ω = 7) can lead to excessive error. Through the comparison in Fig. 3.11, when ω = 5, the RSS prediction is the same to the observation in about 30% of the prediction and most cases the RSS deviation is less than 10 dBm, which is the range of typical signal fluctuation in an indoor environment. Thus, the attenuation factor as 5 is selected to be used for the optimisation model of AP placement.
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WiFi/PDR integrated indoor localization using unconstrained smartphones

WiFi/PDR integrated indoor localization using unconstrained smartphones

This paper proposes a novel-integrated localization ap- proach based on two UKFs. One UKF is used for ultim- ate location estimation, whose state and measurement models are PDR and WiFi localization, respectively. For PDR state model, we develop another UKF for device attitude tracking, which renders an improved user head- ing estimation. By constructing the relationship between quaternion vector and user heading, the error covariance of heading estimation can be also accurately obtained. For measurement model, we adopt the kernel density estimation (KDE) method to obtain measurement noise statistics, rather than set them empirically as previous approaches. Besides, in order to adapt the unconstrained daily use of smartphones, we propose a robust carrying position recognition method based on orientation inva- riant features. Parameter settings of walking step length estimation and walking step detection are tuned accor- ding to the recognized carrying positions. Experimental results show that our WiFi/PDR-integrated localization approach may improve traditional approaches in terms of reliability and localization accuracy.
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A Microscopic Look at WiFi Fingerprinting for Indoor Mobile Phone Localization in Diverse Environments

A Microscopic Look at WiFi Fingerprinting for Indoor Mobile Phone Localization in Diverse Environments

In this paper, we take a microscopic look at the well- known WiFi fingerprinting approach when applied for indoor mobile phone localization. Specifically, we examine the impact of various aspects underlying a WiFi fingerprinting system, including: the definition of a fingerprint, run-time location estimation algorithms, frequency band and presence of virtual access points (VAPs). Our investigation considers several different real indoor environments ranging from a multi-storey office building to shopping centers of different sizes. Seven different definitions of fingerprints are considered that span RSSI based, AP visibility based and combinations of both. With respect to location estimation algorithms, we compare three different deterministic techniques (including the often used Euclidean distance based nearest neighbor method) with two probabilistic techniques that use Gaussian and Log-normal distributions for RSSI modeling.
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Towards Modeling Privacy in WiFi Fingerprinting Indoor Localization and its Application

Towards Modeling Privacy in WiFi Fingerprinting Indoor Localization and its Application

People spend significant amounts of their time in public indoor environments including shopping malls, libraries, airports, university campuses, etc. This has boosted the interest towards various indoor location- based applications[1, 2] such as indoor-navigation or elderly assistance and emergency responding. How- ever, in an indoor environment, the traditional Global Positioning System (GPS) may be not available due to weak signal strengths caused by blocking constructions. To obtain a location in a building, a client has to rely on certain indoor location services (ILS) provided by some server of the build- ing. The most widely used approach for ILS is the one based on the WiFi fingerprinting technique [3, 4, 5, 6, 7, 8, 9, 10, 11]. This method is very effective and popular because it uses an existing WiFi infrastructure of a building. For a WiFi fingerprint based ILS, the server holds a geo-location database (e.g. [12, Table 1]) containing signal strengths of WiFi access points (AP) in various reference locations, as explained in Section 3. Roughly speaking, a client measures the signal strengths of WiFi APs in the
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Visual landmark sequence based indoor localization

Visual landmark sequence based indoor localization

This paper presents a method that uses common objects as land- marks for smartphone-based indoor localization and navigation. First, a topological map marking relative positions of common ob- jects such as doors, stairs and toilets is generated from floor plan. Second, a computer vision technique employing the latest deep learning technology has been developed for detecting common indoor objects from videos captured by smartphone. Third, second order Hidden Markov model is applied to match detected indoor landmark sequence to topological map. We use videos captured by users holding smartphones and walking through corridors of an office building to evaluate our method. The experiment shows that computer vision technique is able to accurately and reliably detect 10 classes of common indoor objects and that second order hidden Markov model can reliably match the detected landmark sequence with the topological map. This work demonstrates that computer vision and machine learning techniques can play a very useful role in developing smartphone-based indoor positioning applications.
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Indoor Localization System Using WiFi RSSI

Indoor Localization System Using WiFi RSSI

Abstract— Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the pastdecades.In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phonesfingerprint-based indoor localization system designedand built to run on mobile phones.Experiments carried out in a single- and a multi-story building revealed that the proposed method could successfully build a precise localization model without any location reference or explicit efforts to collect labeled samples. By exploiting user motions from mobile phones,we successfully remove the site survey process of traditional approaches,while at the same time,competitive localization accuracy.
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A Markov Model for Dynamic Behavior of ToA-Based Ranging in Indoor Localization

A Markov Model for Dynamic Behavior of ToA-Based Ranging in Indoor Localization

We categorize the ranging error into four di ff erent classes and present clarifications as to the statistical occurrence of each class of ranging errors. Furthermore, we provide dis- tributions to model typical values of ranging error observed in each class of receiver locations. Next, we link each class of ranging errors to a state of a Markov process which can be used for the simulation of spatial behavior of the class of ranging errors for a mobile user randomly traveling in a building. Finally, we provide a method to statistically ex- tract the average probabilities of residing in a certain state for the building under study. The presented model for dy- namic behavior of ranging error is essential for the design and performance evaluation of tracking capabilities of the proposed algorithms for indoor localization. The parameters of the Markov model are analytically derived from the results of the UWB measurement conducted on the third floor of the Atwater Kent laboratories (AK Labs) at Worcester Poly- technic Institute (WPI). The parameters of distributions of ranging error in each Markov state are extracted from empir- ical data collected from a measurement calibrated ray trac- ing (RT) algorithm simulating the same o ffi ce environment. The commonly used RT software, previously used in litera- ture for communication purposes [23, 24], provides the ra- dio propagation of the indoor environment in which reflec- tion and transmission are the dominant mechanisms. It has been shown that the existing RT software can be a useful and practical simulation tool to assess the behavior of ranging er- ror in indoor environments [9].
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MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System

MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System

low power levels, causing weak signal strength indoors. Thus, the building of systems that can afford satisfactory location estimates indoors is important. Such a system is called an indoor positioning system (IPS). Services that depend on IPSs are known as indoor location-based services (ILBSs) [ 2 ]. Instances of such services include ILBSs for travelers who need tools or position guidance services to pinpoint significant exotic sites of interest [ 3 ]. Another instance can be found in huge shopping areas and malls [ 4 ], where customers generally wish to quickly locate a restaurant or shop that has an outstanding user rating. Additionally, in hospitals and clinics, it is necessary to track the locations of wandering patients in real time in order to locate them quickly and give them timely treatment. Also, in distribution depots, staff requires tracking devices in order to detect the location of goods in real time. Moreover, in art displays, visitors may request to look at the most fascinating paintings located within a museum, and so on. As mentioned, it is not guaranteed that the GPS will work correctly in an intricate indoor region, since satellite signals are often obstructed or very weak. Additionally, in comparison with outdoor areas, greater location resolutions are needed to locate users in indoor areas [ 2 ]. This presents new issues in the construction of systems for indoor positioning on the basis of time-crucial constraints, high accuracy requirements, and efficiency of energy use. In fact, new developments in mobile and wireless technology have commenced a new era of indoor positioning. WiFi positioning is presently among the most potent techniques for localization, and is based on smart devices’ detection and computing. The reasons for this are the availability of the WiFi infrastructure in nearly every indoor area and the WiFi detection feature available in almost all smart devices.
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Barometer-Assisted 3D Indoor WiFi Localization for Smart Devices-Map Selection and Performance Evaluation

Barometer-Assisted 3D Indoor WiFi Localization for Smart Devices-Map Selection and Performance Evaluation

In this thesis, we present an approach to make intruder detection by analyzing GPS data and make multi-floor detection by using barometer in smart devices. We design scenarios on different floors in Atwater Kent laboratory and conduct series of experiments to collect data. By relating the estimation error with the LOA satel- lite number, it shows that estimation becomes more accurate as the LOS satellite number becomes greater. Based on the pressure-height physical law, we take the first derivative of the barometer and use pressure variance to detect floor transition. The handover algorithms are used to automatically detect intruder and multi-floor transition, and the experiment show that the algorithm performs well in indoor building and for any type of transport modes(stairs and elevators). To precisely identify which floor, we also consider noise, device bias and time difference in our pressure-height model.
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Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Pedestrian Dead Reckoning (PDR) positioning along with the use of the Inertial Measurement Unit that is built into a smartphone is widely used in navigation applications in indoor environments. Using PDR, a pedestrian’s next position can be calculated when the starting position, heading information, and displacement are known. Many researchers use a step length model combined with direction information [13,14] to calculate the displacement of indoor pedestrians. However, the peak detection algorithm [15,16], which is commonly used for counting steps, cannot work well because of the large error of a smartphone. A new step counting method based on auto-correlation analysis of the sensor data is explored here. Compared with the traditional peak detection algorithm, it can clearly reduce the influence of step counting result errors caused by different mobile phone locations and motion postures of pedestrians.
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CSI based fingerprinting for indoor localization using LTE Signals

CSI based fingerprinting for indoor localization using LTE Signals

channel gains per subcarrier that can be extracted by com- modity hardware. In this work, more generally, we call CSI a vector of channel gains per subcarrier that represents an estimate of the channel frequency response of the prop- agation channel. Therefore, this paper proposes an LTE signal fingerprinting localization method that uses CSI as fingerprint. Moreover, the proposed approach is differ- ent from other CSI-related approaches that can be found in the literature, mainly employed for indoor localization and based on WiFi signals, where fingerprints are vec- tors containing the values of measured CSI. We propose to use as fingerprints not only the vectors of CSI but also some “descriptors” of the “shape” of the CSI calculated on these vectors. This would greatly reduce the require- ments in terms of memory for the database and also the computational complexity of the matching phase. It is also worth outlining that the proposed method extracts the CSI from signalling messages meaning that the mobile device does not need to have any subscription with any mobile operator. As a matter of fact, the device can receive the signalling messages of different eNodeBs regardless the specific operator.
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Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Pedestrian Dead Reckoning (PDR) positioning along with the use of the Inertial Measurement Unit that is built into a smartphone is widely used in navigation applications in indoor environments. Using PDR, a pedestrian’s next position can be calculated when the starting position, heading information, and displacement are known. Many researchers use a step length model combined with direction information [13,14] to calculate the displacement of indoor pedestrians. However, the peak detection algorithm [15,16], which is commonly used for counting steps, cannot work well because of the large error of a smartphone. A new step counting method based on auto-correlation analysis of the sensor data is explored here. Compared with the traditional peak detection algorithm, it can clearly reduce the influence of step counting result errors caused by different mobile phone locations and motion postures of pedestrians.
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Survey on Localization of Smartphone User in an Indoor Environment Using WIFI and Navigation through Layout of the Floor Plans

Survey on Localization of Smartphone User in an Indoor Environment Using WIFI and Navigation through Layout of the Floor Plans

A Wi-Fi based technology is being used in the system here to provide a user with the facility of indoor navigation using a smartphone. First of all the maps of the floor plans which are already present on the server at the premises are downloaded by users. These maps or floor plans are further plotted and stored by the system we are using. These helps the user in efficiently using there smartphones to navigate through the structure very easily. As this application also provides the feature of saving the location helps users in finding back the place very easily and accurately once it is saved. For example a person can easily return back to the parking space where they parked there vehicle without any difficulty. This application uses triangulation method which requires the signal strength from at least three Wi-Fi routes in the building strong enough to pin point the exact location of the user’s position and also of those outlets that user wants to visit. User can also find out about any current and upcoming events held in the campus by looking at the calendar provided in the application. Furthermore using the contact list this application enables the user to find if any of their contacts are present in the same area that they are in if the other users have the same application installed in their respective handsets.
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Simulation Based WiFi Fingerprinting for Indoor Localization

Simulation Based WiFi Fingerprinting for Indoor Localization

provide users locations very accurately but its signals are often blocked and absorbed by walls or other obstacles at indoor scenarios. Rosums TV-GPS is an enhanced positioning technique, which works both indoor and outdoor scenarios. It uses the Time Difference of Arrival (TDOA) approach applied to TV signals to estimate the position. As said in [2], it needs additional hardware for television transmitter towers to achieve precise and proper time synchronization between transmitters and receivers. Another interesting localization approach is Japans Indoor Messaging System (IMES), which is an important part of the regional Quasi-Zenith Satellite System (QZSS) project. It uses GPS signals and provides precise positioning because it employs terrestrial transmitter equipments and beacons to assist the whole localization process [3]. All the above mentioned localization systems are not suitable for proposed method, which is mentioned in this paper. The motivation is linked to three main factors: (i) the high cost of the network infrastructure for metropolitan-scale coverage; (ii) the necessity of additional modules for mobile devices, which increases implementation costs; (iii) to develop a multipurpose system which can be used as an ID card and for tracking inside campus rather than going for a smartphones for positioning system. A large number of handheld devices now have Bluetooth functionality, but it always takes a long time to read signal values, which is not practical for a mobile positioning system. Positing using RFID is another promising filed for indoor positioning, but Active RFID receivers are very expensive [4]. Wi-Fi is a ubiquitous technology that is broadly accepted by users and they are freely available nowadays. It represents a cost efficient and reliable technique that indoor positioning services can employ. Analysis of the current signal measurement methods included, the angle of arrival (AoA), received signal strength (RSS), time of arrival (ToA) and time difference of arrival (TDoA). The techniques of AoA, ToA, TDoA require a degree of time synchronization that is difficult to achieve using inexpensive off-the shelf WLAN hardware [5]. However, RSS indicating capable equipment is widely available in Wi-Fi devices. Utilizing existing WLAN infrastructure by reading RSS is a cost effective solution for this problem. Wi-Fi fingerprinting is a localization technique used for positioning with wireless access points is based on measuring the intensity of the Received Signal Strength [6].So this paper mainly explains wifi fingerprinting technology for indoor positioning by using a hardware rather than smartphones .
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Super resolution WiFi indoor localization and tracking

Super resolution WiFi indoor localization and tracking

In this paper, we presented a framework for accurate multipath indoor localization and tracking of mobile nodes. Accurate delay estimation of OFDM signals based on the IEEE 802.11a standard is performed via the super resolution MUSIC technique and tracking is done via PF. The combined MUSIC- PF algorithm begins with the estimation of the channel transfer function, which is then used to generate autocorrelation matrix, on which the MUSIC algorithm operates for delay estimation. Once the delay (and hence the distance) information from all BSs is made available, it is used in the observation model for PF tracking. A simplistic constant velocity motion model is considered for indoor MD movement. It is shown via simulation result that the performance of the MUSIC-PF algorithm supersedes that of CC-PF in which the multipath delay estimation is done via a conventional CC.
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Real Time Location based Tracking using WIFI Signals

Real Time Location based Tracking using WIFI Signals

The latest method of tracking and locating of client-based mobile phone which will eliminate the limitation of the in- built GPS phones, the term Wi-Fi was created by an organization called the Wi-Fi association to declare products that belong to a class of wireless local area network (WLAN) device based on the IEEE 802.11 standard. A product that passes the association test is given the tag. Any Wi-Fi licensed devices are able to connect and communicate with one another by mean of Wi-Fi.

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