Relevant bibliographies by topics / Indoor localization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Indoor localization'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Indoor localization"

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Rohmat Rose, Nur Diana, and Low Tan Jung. "Comparison of Indoor Localization Scheme for Multistory Environment." Advanced Journal of Technical and Vocational Education 4, no. 3 (September 30, 2020): 8–13. http://dx.doi.org/10.26666/rmp.sjtve.2020.3.2.

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Outdoor localization nowadays is a part of our daily life. However, such useful localization method as the Global Positioning System (GPS) fails inside buildings. Popular alternative is to utilize the Indoor Positioning System (IPS) technologies that are accessible indoors. There is a wide range of IPS technologies that can be used within an indoor environment. Moreover, more attention has been given to IPS for multistory buildings recently. In indoor localization, different techniques and methods are used for distance and position estimation. This paper will focus on indoor localization technologies and the comparison of indoor localization methods focusing on multistory environment which will improve the localization accuracy through various models and techniques.
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Abkari, Safae El. "Wireless Indoor Localization Using Fingerprinting Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2597–602. http://dx.doi.org/10.5373/jardcs/v12sp7/20202394.

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Varshavsky, Alex, Eyal de Lara, Jeffrey Hightower, Anthony LaMarca, and Veljo Otsason. "GSM indoor localization." Pervasive and Mobile Computing 3, no. 6 (December 2007): 698–720. http://dx.doi.org/10.1016/j.pmcj.2007.07.004.

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Azrad, Syaril, Mohammad Fadhil, Farid Kendoul, and Kenzo Nonami. "Quadrotor UAV Indoor Localization Using Embedded Stereo Camera." Applied Mechanics and Materials 629 (October 2014): 270–77. http://dx.doi.org/10.4028/www.scientific.net/amm.629.270.

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Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors inGlobal Positioning System (GPS)-denied environment such as indoors has been done using varioustechniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonicsensors installed indoor or applied indoor environment modification such as patching (InfraRed) IR and visual markers. While these systems have high accuracy for the UAV localization, theyare expensive and have less practicality in real situations. We propose a system consisting of a stereocamera embedded on a quadrotor UAV for indoor localization. The optical flow data from the stereocamera then are fused with attitude and acceleration data from our sensors to get better estimationof the quadrotor location. Using stereo camera capabilities the quadrotor altitude are estimated usingSIFT Feature Stereo Matching are used in addition to the altitude estimation computed using opticalflow. To avoid latency due to computational time, image processing and the quadrotor control areprocessed threads and core allocation.
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Ingabire, Winfred, Hadi Larijani, Ryan M. Gibson, and Ayyaz-UI-Haq Qureshi. "LoRaWAN Based Indoor Localization Using Random Neural Networks." Information 13, no. 6 (June 16, 2022): 303. http://dx.doi.org/10.3390/info13060303.

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Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.
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Kim Geok, Tan, Khaing Zar Aung, Moe Sandar Aung, Min Thu Soe, Azlan Abdaziz, Chia Pao Liew, Ferdous Hossain, Chih P. Tso, and Wong Hin Yong. "Review of Indoor Positioning: Radio Wave Technology." Applied Sciences 11, no. 1 (December 30, 2020): 279. http://dx.doi.org/10.3390/app11010279.

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The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.
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Weiping Zhu, Weiping Zhu, and Xiaoling Cheng Weiping Zhu. "Indoor Localization Method of Mobile Educational Robot Based on Visual Sensor." 網際網路技術學刊 24, no. 1 (January 2023): 205–15. http://dx.doi.org/10.53106/160792642023012401019.

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<p>This article aims to study the mobile positioning method of mobile educational robots indoors. In order for robots to be able to unblocked indoors, they can avoid obstacles well. Vision sensors are the direct source of information for the entire machine vision system, and are mainly composed of one or two graphics sensors, sometimes accompanied by light projectors and other auxiliary equipment. This paper presents an indoor positioning method for mobile educational robots based on visual sensors. Build some models to compare which algorithm is more in line with the positioning of indoor mobile educational robots. The experimental results in this paper show that the positioning accuracy of the optical flow meter and the odometer on the short-haired carpet is equivalent (both are less than the index 4.52%); the positioning error of the optical flowmeter on the long-haired carpet is the largest 7%, and the positioning error of the odometer is the largest it reached 83%. The error of the algorithm positioning method after the visual odometer fusion is obviously smaller than that of the optical flow method. This shows that the algorithm after visual process fusion is more suitable for indoor mobile educational robot positioning than this optical flow method.</p> <p>&nbsp;</p>
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Cheon, Sooyoung, Daekug Lee, and Ah-Rim Joo. "Machine Learning Indoor Localization Study Based on RSSI Data." Korean Data Analysis Society 25, no. 6 (December 31, 2023): 2159–70. http://dx.doi.org/10.37727/jkdas.2023.25.6.2159.

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With the recent development of science and technology, localization technology has made it possible to provide customized mobile services based on real-time location information to users through smartphones and Internet of Things devices. In particular, GPS-based real-time localization is commonly used. However, unlike outdoors, not only does the GPS reception rate decrease significantly indoors, but it is also difficult to accurately measure the user's indoor location using only GPS latitude and longitude information. In this study, we propose to measure the user's indoor location using RSSI (received signal strength indicator). For this purpose, an indoor localization study was conducted by applying machine learning classifiers, SVM, decision tree, ExtraTrees, random forest, and KNN to RSSI data. Among the machine learning classifiers, random forest exhibited the best performance. Therefore, we applied random forest-based RFE to extract features from RSSI data. We confirmed that even with a smaller amount of data, it was possible to achieve more accurate indoor positioning. In addition, it was confirmed through regression analysis that latitude and longitude can be estimated from RSSI data, so that indoor as well as outdoor locations can be estimated.
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Yan, Kun, Hsiao-Chun Wu, Shih-Hau Fang, Chiapin Wang, Shaopeng Li, and Lixuan Zhang. "Indoor Femtocell Interference Localization." IEEE Transactions on Wireless Communications 19, no. 8 (August 2020): 5176–87. http://dx.doi.org/10.1109/twc.2020.2990228.

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Lymberopoulos, Dimitrios, Jie Liu, Xue Yang, Romit Roy Choudhury, Souvik Sen, and Vlado Handziski. "Microsoft Indoor Localization Competition." GetMobile: Mobile Computing and Communications 18, no. 4 (January 14, 2015): 24–31. http://dx.doi.org/10.1145/2721914.2721923.

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Dissertations / Theses on the topic "Indoor localization"

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Gao, Xiangjian. "UWB Indoor Localization System." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10813674.

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This thesis report has introduced the UWB Indoor Localization System. In the beginning, this thesis report has explained the Indoor Localization System and presented existing techniques (such as Wi-Fi and Bluetooth) to construct an Indoor Localization System. Then, this thesis report has discussed the Ultra Wideband Radio fundamentals to analyze its construction and operating mechanism. During the transmission, the UWB signals will pass an additive white Gaussian noise channel with multipath effects, which cause errors in the values of bits. This thesis report has studied different solutions (such as Modulation Methods and Rake Receiver) to improve the bit error rate in different situations (such as Multipath-free AWGN channel). Next, this thesis report utilizes the UWB Radio fundamentals to show and compare different positioning algorithms (such as TOA and AOA). This thesis report focuses on TOA algorithm. For TOA algorithm, this thesis report has analyzed the IEEE UWB standards and the UWB Radio fundamentals to present and compare different types of receivers. Finally, this thesis report has studied algorithms (such as WLS) to solve non-linear equations to find the position of a mobile station with NLOS effects. In this thesis report, an algorithm (removing excess delay) has been used to mitigate NLOS effects with the simulation based on IEEE 802.15.4a channels. The simulation results are shown in chapter 12, and the average positioning error is around 7 cm.

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Barac, Daniel. "Localization algorithms for indoor UAVs." Thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217.

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The increased market for navigation, localization and mapping system has encouraged the research to dig deeper into these new and challenging areas. The remarkable development of computer soft- and hardware have also opened up many new doors. Things which more or less where impossible ten years ago are now reality. The possibilities of using a mathematical approach to compensate for the need of expensive sensors has been one of the main objectives in this thesis. Here you will find the basic principles of localization of indoor UAVs using particle filter (PF) and Octomaps, but also the procedures of implementing 2D scanmatching algorithms and quaternions. The performance of the algorithms is evaluated using a high precision motion capture system. The UAV which forms the basis for this thesis is equipped with a 2D laser and an inertial measurement unit (IMU). The results show that it is possible to perform localization in 2D with centimetre precision only by using information from a laser and a predefined Octomap.
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Zaharans, Eriks. "Indoor robot localization and collaboration." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102604.

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The purpose of this thesis is to create an indoor rescue scenario with multiple self-localizing robots that are able to collaborate for a victim search. Victims are represented by RFID tags and detecting them combined with an accurate enough location data is considered as a successful finding. This setup is created for use in a laboratory assignment at Linköping University. We consider the indoor localization problem by trying to use as few sensors as possible and implement three indoor localization methods - odometry based, passive RFID based, and our approach by fusing both sensor data with particle filter.The Results show that particle filter based localization performs the best in comparison to the two other implemented methods and satisfies the accuracy requirements stated for the scenario. The victim search problem is solved by an ant mobility (pheromone-based) approach which integrates our localization method and provides a collaborative navigation through the rescue area. The purpose of the pheromone mobility approach is to achieve a high coverage with an acceptable resource consumption.Experiments show that area is covered with approximately 30-40% overhead in traveled distance comparing to an optimal path.
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Pathapati, Subbu Kalyan Sasidhar. "Indoor Localization Using Magnetic Fields." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103371/.

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Indoor localization consists of locating oneself inside new buildings. GPS does not work indoors due to multipath reflection and signal blockage. WiFi based systems assume ubiquitous availability and infrastructure based systems require expensive installations, hence making indoor localization an open problem. This dissertation consists of solving the problem of indoor localization by thoroughly exploiting the indoor ambient magnetic fields comprising mainly of disturbances termed as anomalies in the Earth’s magnetic field caused by pillars, doors and elevators in hallways which are ferromagnetic in nature. By observing uniqueness in magnetic signatures collected from different campus buildings, the work presents the identification of landmarks and guideposts from these signatures and further develops magnetic maps of buildings - all of which can be used to locate and navigate people indoors. To understand the reason behind these anomalies, first a comparison between the measured and model generated Earth’s magnetic field is made, verifying the presence of a constant field without any disturbances. Then by modeling the magnetic field behavior of different pillars such as steel reinforced concrete, solid steel, and other structures like doors and elevators, the interaction of the Earth’s field with the ferromagnetic fields is described thereby explaining the causes of the uniqueness in the signatures that comprise these disturbances. Next, by employing the dynamic time warping algorithm to account for time differences in signatures obtained from users walking at different speeds, an indoor localization application capable of classifying locations using the magnetic signatures is developed solely on the smart phone. The application required users to walk short distances of 3-6 m anywhere in hallway to be located with accuracies of 80-99%. The classification framework was further validated with over 90% accuracies using model generated magnetic signatures representing hallways with different kinds of pillars, doors and elevators. All in all, this dissertation contributes the following: 1) provides a framework for understanding the presence of ambient magnetic fields indoors and utilizing them to solve the indoor localization problem; 2) develops an application that is independent of the user and the smart phones and 3) requires no other infrastructure since it is deployed on a device that encapsulates the sensing, computing and inferring functionalities, thereby making it a novel contribution to the mobile and pervasive computing domain.
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KOLEDOYE, MOSES AYODELE. "Cooperative Indoor Localization under Uncertainties." Doctoral thesis, Università degli studi di Pavia, 2019. http://hdl.handle.net/11571/1244487.

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Localization of wireless nodes in GPS-denied spaces is being applied in a number of scenarios such as sport teams requiring positioning data for post-match analysis, robot teams carrying out a common task in an indoor environment, among other similar applications. These applications would usually involve a sizable number of participating nodes making scalability a fundamental requirement of the localization setup under the constraints of energy efficiency and position update time. Adopting techniques that are resilient to uncertainties in the deployment region of the nodes is important for the overall accuracy of the setup. In this work, we focus on a range-based cooperative localization technique where nodes are distinguished as either fixed (anchors) or mobile (tags), and are subject to uncertainties in the environment. Cooperation here implies that the positions of all nodes are computed simultaneously using a joint pairwise distance information while uncertainty refers to any known condition that degrades localization accuracy. These uncertainties are present in the form of a) missing distance measurements; b) obstacles in the deployment region; and c) stochasticity in measurements for cases where Radio Frequency (RF) signal strength is employed for range estimation. The missing distances may be due to either tags being passive or tags acting as transmitters. For this, we propose a specialized form of Multidimensional Scaling (MDS) that tackles the problem by neglecting tag-to-tag interactions while inferring tag positions directly from those of anchors. Furthermore, obstacles in the deployment region force signals to travel in nonline-of-sight (NLOS) paths often leading to a lengthening of range estimates. For this, we develop a novel approach that reuses an intrinsic property of anchored MDS to cooperatively estimate NLOS biases in the range estimates. The problem is formulated as a constrained-optimization problem whose solution provides positions with improved accuracy and can be solved by Sequential Quadratic Programming (SQP). The approach works entirely at the application layer and is neither concerned with the probability distribution of LOS/NLOS nor any other a priori knowledge about the environment. Experimental results show that position errors can be reduced by up to 28% for a set up to 4 fixed and 3 mobile nodes. In the final discourse relating to uncertainties, we examine lightweight filtering techniques for smoothening Received Signal Strength (RSS) measurements to render them more suitable for range estimation. We formulate the expected values of range (in terms of the Cramér-Rao bound) when estimated directly from raw measurements using an unbiased estimator and compare with range estimates from the filtered measurements. Results show that applying a suitable filtering technique can significantly improve the accuracy of range estimation from raw RSSI measurements. In the last part of this work, we present an open discussion on design considerations for scalable indoor localization deployments.
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Lv, Xiaowei. "Indoor localization in wireless sensor networks." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0009/document.

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Ce manuscrit est dédié à la résolution du problème de localisation dans les réseaux de capteurs sans fil mobiles. Les méthodes développées se basent principalement sur des caractéristiques de fingerprints ainsi que sur des informations de mobilité. Les premières s'attaquent aux valeurs de RSSI entre capteurs tandis que les deuxièmes prennent en considération la mobilité des capteurs mesurée à l'aide d'accéléromètres et de gyroscopes. La combinaison des données collectées est effectuée dans le cadre de l'analyse par intervalles, ou bien du filtrage de Kalman. Les travaux proposés introduisent des modèles de mobilité d'ordres un, deux ou trois, permettant d'approximer au mieux les trajectoires des capteurs à l'aide des accélérations mesurées. Ceux-là sont couplés à l'algorithme des K plus proches voisins, d'abord dans un système centralisé. Ensuite, les modèles de mobilités sont améliorés pour prendre en compte les rotations des nœuds. Une méthode de localisation décentralisée est également proposée dans ce qui suit, s'adaptant au mécanisme fonctionnel des réseaux de capteurs de grande échelle. Enfin, ce manuscrit propose une méthode de zonage visant à déterminer les zones dans lesquelles les capteurs résident. La méthode proposée aborde le problème de zonage en utilisant à la fois la théorie des fonctions de croyance et l'analyse par intervalles
This thesis is dedicated to solve the localization problem in mobile wireless sensor networks. It works mainly with fingerprints features and inertial movements information. The former tackles the RSSIs values between sensors while the latter deals with the objets movement attitude by using accelerometer and gyroscope. The combination of both information is performed in terms of interval analysis, or Kalman filtering. The proposed work introduces three orders mobility models to approximate nodes trajectories using accelerations, combined then to the weighted K nearest neighbors algorithm in a centralized scheme. Then the mobility models are extended up to the inertial information taking into consideration the rotations of the nodes. A decentralized localization method is also proposed in the following in view of the working mechanism of large scale sensor networks. Finally, this thesis proposes a zoning localization method aiming at determining the zones in which the nodes reside. The proposed method addresses the zoning problem by using both the belief functions theory and the interval analysis
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Poston, Jeffrey Duane. "ILoViT: Indoor Localization via Vibration Tracking." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82871.

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Indoor localization remains an open problem in geolocation research, and once this is solved the localization enables counting and tracking of building occupants. This information is vital in an emergency, enables occupancy-optimized heating or cooling, and assists smart buildings in tailoring services for occupants. Unfortunately, two prevalent technologies---GPS and cellular-based positioning---perform poorly indoors due to attenuation and multipath from the building. To address this issue, the research community devised many alternatives for indoor localization (e.g., beacons, RFID tags, Wi-Fi fingerprinting, and UWB to cite just a few examples). A drawback with most is the requirement for those being located to carry a properly-configured device at all times. An alternative based on computer vision techniques poses significant privacy concerns due to cameras recording building occupants. By contrast, ILoViT research makes novel use of accelerometers already present in some buildings. These sensors were originally intended to monitor structural health or to study structural dynamics. The key idea is that when a person's footstep-generated floor vibrations can be detected and located then it becomes possible to locate persons moving within a building. Vibration propagation in buildings has complexities not encountered by acoustic or radio wave propagation in air; thus, conventional localization algorithms are inadequate. ILoVIT algorithms account for these conditions and have been demonstrated in a public building to provide sub-meter accuracy. Localization provides the foundation for counting and tracking, but providing these additional capabilities confronts new challenges. In particular, how does one determine the correct association of footsteps to the person making them? The ILoViT research created two methods for solving the data association problem. One method only provides occupancy counting but has modest, polynomial time complexity. The other method draws inspiration from prior work in the radar community on the multi-target tracking problem, specifically drawing from the multiple hypothesis tracking strategy. This dissertation research makes new enhancements to this tracking strategy to account for human gait and characteristics of footstep-derived multilateration. The Virginia Polytechnic Institute and State University's College of Engineering recognized this dissertation research with the Paul E. Torgersen Graduate Student Research Excellence Award.
Ph. D.
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Woolard, Americo Giuliano. "Supplementing Localization Algorithms for Indoor Footsteps." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78698.

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The data rich nature of instrumented civil structures has brought attention to alternative applications outside of the traditional realm of structural health monitoring. An interest has been raised in using these vibration measurements for other applications such as human occupancy. An example of this is to use the vibrations measured from footsteps to locate occupants within a building. The localization of indoor footsteps can yield several benefits in areas such as security and threat detection, emergency response and evacuation, and building resource management, to name a few. The work described herein seeks to provide supplementary information to better define the problem of indoor footstep localization, and to investigate the use of several localization techniques in a real-world, operational building environment. The complexities of locating footsteps via indoor vibration measurements are discussed from a mechanics perspective using prior literature, and several techniques developed for localization in plate structures are considered for their applicability to indoor localization. A dispersion compensation tool is experimentally investigated for localization in an instrumented building. A machine learning approach is also explored using a nearest neighbor search. Additionally, a novel instrumentation method is designed based on a multi-point coupling approach that provides directional inference from a single point of measurement. This work contributes to solving the indoor footstep localization problem by consolidating the relevant mechanical knowledge and experimentally investigating several potential solutions.
Ph. D.
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Pettersson, Rasmus. "Continuous localization in indoor shifting environment." Thesis, Uppsala universitet, Fasta tillståndets elektronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-326270.

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In this Master Thesis different approaches to mobile localization within construction environments are investigated. At first an overview of different sensors commonly used within localization is presented together with different map representations and a system consisting of a laser scanner and wheel encoders is chosen. The hardware is prepared for the open source ROS environment and three different algorithms for localization are tested. Two algorithms, Gmapping and HectorSLAM, used for Simultaneous Localization and Mapping, are compared. The best map is then used by a Monte Carlo localization algorithm, AMCL, for autonomous navigation. It is found that HectorSLAM produces the most accurate map, given that the grid refinement level is fine enough for the environment. It is also found that the maximum Kullback Leiber distance, used in AMCL, needs to be calibrated in order to perform a sufficient navigation.
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Kim, Gukhwan. "Designing Robust Fiduciary Markers for Indoor Localization." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28703.

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In this thesis, two fiduciary markers are newly designed as an alternate method for an indoor localization system. The main purpose of this work is to design more robust fiduciary markers than currently available ones in aspect of data restoration ability against data loss. In addition, achieving more accurate pose tracking performance is another goal of this work. Currently available fiduciary markers may not be ideal for indoor localization due to their lack of robustness against occlusions. Thus, proposed fiduciary markers must provide better performance under occlusions with respect to a marker detection rate. By adopting digital encoding/decoding techniques combined with using multiple sub-markers or PRMVS pattern, proposed ones achieved a better marker detection rate under partial occlusions. In addition, the experimental results show that proposed fiduciary markers provide more accurate camera pose measurement than other fiduciary markers.
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Books on the topic "Indoor localization"

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. Wireless Indoor Localization. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2.

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Mautz, Rainer. Indoor positioning technologies. Zürich: Schweizerische Geodätische Kommission, 2012.

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Tiku, Saideep, and Sudeep Pasricha, eds. Machine Learning for Indoor Localization and Navigation. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26712-3.

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Chessa, Stefano, and Stefan Knauth, eds. Evaluating AAL Systems Through Competitive Benchmarking. Indoor Localization and Tracking. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33533-4.

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Chen, Leian. Signal Processing and Machine Learning Methods for Internet of Things: Smart Energy Generation and Robust Indoor Localization. [New York, N.Y.?]: [publisher not identified], 2022.

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1974-, Zou Wei, ed. Shi nei yi dong shi fu wu ji qi ren de gan zhi, ding wei yu kong zhi. Beijing: Ke xue chu ban she, 2008.

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Stefan, Knauth, and SpringerLink (Online service), eds. Evaluating AAL Systems Through Competitive Benchmarking. Indoor Localization and Tracking: International Competition, EvAAL 2011, Competition in Valencia, Spain, July 25-29, 2011, and Final Workshop in Lecce, Italy, September 26, 2011. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Visible Light Communication Based Indoor Localization. Taylor & Francis Group, 2019.

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Kavehrad, Mohsen, and Reza Aminikashani. Visible Light Communication Based Indoor Localization. CRC Press, 2019. http://dx.doi.org/10.1201/9780429355806.

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Kavehrad, Mohsen, and Reza Aminikashani. Visible Light Communication Based Indoor Localization. Taylor & Francis Group, 2019.

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Book chapters on the topic "Indoor localization"

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Youssef, Moustafa. "Indoor Localization." In Encyclopedia of GIS, 547–52. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_622.

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Youssef, Moustafa. "Indoor Localization." In Encyclopedia of GIS, 1–7. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_622-2.

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Youssef, Moustafa. "Indoor Localization." In Encyclopedia of GIS, 1004–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_622.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Background and Overview." In Wireless Indoor Localization, 3–16. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_1.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Research Summary and Future Directions." In Wireless Indoor Localization, 219–20. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_10.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Mobile Crowdsourcing and Inertial Sensing." In Wireless Indoor Localization, 17–30. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_2.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Radio Map Construction Without Site Survey." In Wireless Indoor Localization, 33–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_3.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Building Tomography: Automatic Floor Plan Generation." In Wireless Indoor Localization, 59–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_4.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Adaptive Radio Map Updating." In Wireless Indoor Localization, 83–107. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_5.

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Wu, Chenshu, Zheng Yang, and Yunhao Liu. "Self-Deployable Peer-to-Peer Navigation." In Wireless Indoor Localization, 109–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0356-2_6.

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Conference papers on the topic "Indoor localization"

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Melamed, Roie. "Indoor localization." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897073.2897074.

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Gruteser, Marco. "Indoor localization." In the 19th annual international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2500423.2500453.

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Marulkar, Sameer, Mrunal Khadilkar, and Veena Prabhu. "Indoor object localization." In 2013 Tenth International Conference on Wireless and Optical Communications Networks - (WOCN). IEEE, 2013. http://dx.doi.org/10.1109/wocn.2013.6616249.

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Buyukcorak, Saliha, Tayfun Erbas, Gunes Karabulut Kurt, and Abbas Yongacoglu. "Indoor localization applications." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830460.

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Abadi, Aharon, Roie Melamed, Eli Packer, and Natalie Shapira. "Smoothing indoor trajectories." In 2016 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2016. http://dx.doi.org/10.1109/icl-gnss.2016.7533845.

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De Melo Neto, Mário, and Gibeon De Aquino Júnior. "A Taxonomy of Technologies for Fingerprint-Based Indoor Localization." In VII Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/sbcup.2015.10174.

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In recent years, the need for indoor localization has increased. Earlier systems have been deployed in order to demonstrate that indoor localization can be done. Many researchers are referring to location estimation as a crucial component in numerous applications. The indoor localization techniques can be classified using the following classes: proximity, fingerprint, triangulation and vision analysis, with the fingerprint class being the most used. This paper presents the results of a literature systematic mapping on fingerprint-based indoor localization aiming to identify the technologies used for this purpose. The selected search strategy returned 1003 papers, which underwent a series of inclusion and exclusion criteria that resulted with 539 articles being accepted. This work identified that the main technology used for indoor localization is the WIFI, followed by ZigBee. As novelty, we propose a taxonomy of technologies used in the context of fingerprint-based indoor localization.
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Palazzi, Claudio E. "Drone Indoor Self-Localization." In MobiSys'15: The 13th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2750675.2750677.

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Kojakian, Viken, Christelle Bloch, Vincent Chapuis, Albert Da Silva, Michel Stenta, Denis Genon-Catalot, Nicolas Fourty, et al. "Firefighter indoor localization (POUCET)." In 2019 IEEE Radio and Antenna Days of the Indian Ocean (RADIO). IEEE, 2019. http://dx.doi.org/10.23919/radio46463.2019.8968931.

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Chan, S. H. Gary. "Toward better indoor localization." In the 1st international workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2025876.2025886.

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Arvai, Laszlo. "Smartwatch Based Indoor Localization." In 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257230.

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Reports on the topic "Indoor localization"

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Naikal, Nikhil, John Kua, and Avideh Zakhor. Image Augmented Laser Scan Matching for Indoor Localization. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada512518.

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Jander, Georg, and Daniel Chamovitz. Investigation of growth regulation by maize benzoxazinoid breakdown products. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600031.bard.

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Introduction Previous research had suggested that benzoxazinoids, a class of defensive metabolites found in maize, wheat, rye, and wild barley, are not only direct insect deterrents, but also influence other areas of plant metabolism. In particular, the benzoxazinoid 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxa- zin-3(4H)- one (DIMBOA) was implicated in: (i) altering plant growth by interfering with auxin signaling, and (ii) leading to the induction of gene expression changes and secondary plant defense responses. The overall goal of this proposal was to identify mechanisms by which benzoxazinoids influence other aspects of plant growth and defense. Specifically, the following hypotheses were proposed to be tested as part of an approved BARD proposal: Benzoxazinoid breakdown products directly interfere with auxin perception Global changes in maize and barley gene expression are induced by benzoxazinoid activation. There is natural variation in the maize photomorphogenic response to benzoxazinoids. Although the initial proposal included experiments with both maize and barley, there were some technical difficulties with the proposed transgenic barley experiments and most of the experimental results were generated with maize. Summary of major findings Previous research by other labs, involving both maize and other plant species, had suggested that DIMBOA alters plant growth by interfering with auxin signaling. However, experiments conducted in both the Chamovitz and the Jander labs using Arabidopsis and maize, respectively, were unable to confirm previously published reports of exogenously added DIMBOA effects on auxin signaling. Nevertheless, analysis of bx1 and bx2 maize mutant lines, which have almost no detectable benzoxazinoids, showed altered responses to blue light signaling. Transcriptomic analysis of maize mutant lines, variation in inbred lines, and responses to exogenously added DIMBOA showed alteration in the transcription of a blue light receptor, which is required for plant growth responses. This finding provides a novel mechanistic explanation of the trade-off between growth and defense that is often observed in plants. Experiments by the Jander lab and others had demonstrated that DIMBOA not only has direct toxicity against insect pests and microbial pathogens, but also induces the formation of callose in both maize and wheat. In the current project, non-targeted metabolomic assays of wildtype maize and mutants with defects in benzoxazinoid biosynthesis were used to identify unrelated metabolites that are regulated in a benzoxazinoid-dependent manner. Further investigation identified a subset of these DIMBOA-responsive compounds as catechol, as well as its glycosylated and acetylated derivatives. Analysis of co-expression data identified indole-3-glycerol phosphate synthase (IGPS) as a possible regulator of benzoxazinoid biosynthesis in maize. In the current project, enzymatic activity of three predicted maize IGPS genes was confirmed by heterologous expression. Transposon knockout mutations confirmed the function of the maize genes in benzoxazinoid biosynthesis. Sub-cellular localization studies showed that the three maize IGPS proteins are co-localized in the plastids, together with BX1 and BX2, two previously known enzymes of the benzoxazinoid biosynthesis pathway. Implications Benzoxazinoids are among the most abundant and effective defensive metabolites in maize, wheat, and rye. Although there is considerable with-in species variation in benzoxazinoid content, very little is known about the regulation of this variation and the specific effects on plant growth and defense. The results of this research provide further insight into the complex functions of maize benzoxazinoids, which are not only toxic to pests and pathogens, but also regulate plant growth and other defense responses. Knowledge gained through the current project will make it possible to engineer benzoxazinoid biosynthesis in a more targeted manner to produce pest-tolerant crops without negative effects on growth and yield.
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