441 research outputs found

    Improving Indoor BlueTooth Localization By Using Bayesian Reasoning To Explore System Parameters

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    With the advent of smaller, more mobile electronic devices, a wide variety of services can now be augmented with the additional context that is provided by positional information. Systems commonly used for outdoor localization, such as GPS, cannot necessarily be used for indoor localization because often, separating a localizing device from system infrastructure with walls and other obstacles lowers accuracy. Instead, indoor localization systems can be deployed to replace the contextual information required for some situated services, that would otherwise be lost when a device moves indoors. For example, the trilateration algorithm that GPS uses to combine distance estimates from satellites can be repeated using Bluetooth (BT) devices spread throughout an environment. The signal strength of a set of beacons can be read by a localizing device, and those signal strengths can be equated to the distance between the localizing device and the beacon. These distances can then be combined using trilateration. A major source of error in such a system is that BT signal strength does not map directly to only one distance. Because microwave frequency propagation is susceptible to multipath effects and antenna direction, two devices at a fixed location can read a variety of signal strengths, which may not map to the ideal line-of-sight calibrated value. Therefore, any given signal strength reading cannot be interpreted as a single distance without introducing the potential for substantial error. One solution is to probabilistically model the relationship between distance and signal strength by modelling BT localization using a Bayesian network. In a Bayesian network, the distance versus signal strength relationship is stored as the conditional probability of a signal strength reading given a specific distance. Using a Bayesian inference algorithm, one can then reason backwards from a signal strength to a probability distribution representing the estimated position of the localizing BT device. In this thesis, I explore some of the effects of modelling BT localization with a Bayesian network. I first extend the probabilistic calibration to include the influence of the relative orientation of device antennae on the attenuation of BT signal strength between them. I then experiment with the effects of the position of a receiver within a discrete spatial bin, and of the proximity of the transmitters to the edges of the discrete space, because both have the potential to reduce the accuracy of localization using discrete variables. I found that neither affected the localization results in a significant, avoidable fashion. I then studied the effects of the scope of calibration, in terms of the number of distance values used, and of the number of beacons used in localization. I found that additional distance values and a smaller minimum distance used in calibration could result in increased BT localization accuracy, whereas many BT localization systems perform little calibration at distances smaller than 2 m. I also found that accuracy increased when the number of beacons was greater than four, and that accuracy did not significantly decrease when the number of beacons was three or fewer; whereas most trilateration systems use only three or four beacons. I conclude in general that a combination of probabilistic trilateration calibration and Bayesian network inference are viable techniques, and could allow for improvements to localization accuracy in a number of areas

    Indoor Positioning for BIM

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    Building Informational Modeling (BIM) is very popular in the construction industry in Norway today, and Omega 365 has created a suite of tools for BIM, including a 3D visualising tool for 3D models of buildings, called a BIMViewer. This tool exists in multiple forms, and one of them is an app for mobile phones, which construction workers carry with them on construction sites. When determining one's own position in the BIMViewer, it may take time to find and select the correct position. This study aims to create a feature for the BIMViewer using new technology, IEEE802.11mc and comparing it with an old method, Wi-Fi received signal strength (RSS) with the Log Distance Path Loss model. In addition, GPS was tried in order to prove it was not usable for this use case and in order to compare it with the other two methods. The main goal is to find a method that is cheap for clients to implement in regards to equipment and installation, but is precise enough to provide a good user experience. Three experiments were conducted for this study, one using only GPS and two for the other two methods. One experiment used only a single floor and the other used two floors. Both of these experiments used only 6 access points and were conducted at NyeSUS, the new hospital in Stavanger which was an active construction zone during the experiments. The experiments showed that GPS was a bad choice for the use case and that both the other methods were usable. The round trip time (RTT) method, which used the IEEE802.11mc measurements was more precise than the RSS method, however suffered from the need for more access points than the RSS method. This study concludes that both the RTT and the RSS methods may be usable, however some improvements would be needed for a truly good user experience. The study also suggests that a mix of the two methods may be beneficial

    RF Localization in Indoor Environment

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    In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

    Improving RSSI based distance estimation for wireless sensor networks

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    In modern everyday life we see gradually increasing number of wireless sensor devices. In some cases it is necessary to know the accurate location of the devices. Most of the usual techniques developed to get this information require a lot of resources (power, bandwidth, computation, extra hardware) which small embedded devices cannot afford. Therefore techniques, using small resources without the need for extra hardware, need to be developed. Wireless sensor networks are often used inside buildings. In such environment satellite positioning is not available. As a consequence, the location computation must be done in network-based manner. In this thesis a received signal strength indicator (RSSI) based distance estimation technique for 802.15.4 network based on CC2431 radio is discussed. In this approach we try to differentiate between good and erroneous measurements by imposing limits based on standard deviation of RSSI and the number of lost packets. These limits are included as a part of the model parameter estimation process. These limits are optimized in order to improve the resulting distance estimates with minimum loss of connectivity information. We experimentally evaluated the merits of the proposed method and found it to be useful under certain circumstances.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

    AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

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    With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

    A review of smartphones based indoor positioning: challenges and applications

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    The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

    A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments

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    Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided
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