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Relevant bibliographies by topics / Signal Strength Estimation

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Academic literature on the topic 'Signal Strength Estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Signal Strength Estimation"

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Li, Kejiong, Peng Jiang, Eliane L. Bodanese, and John Bigham. "Outdoor Location Estimation Using Received Signal Strength Feedback." IEEE Communications Letters 16, no. 7 (2012): 978–81. http://dx.doi.org/10.1109/lcomm.2012.050912.111805.

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Yehya, Tamer, Yahya Mohasseb, and Ashraf Mahran. "Position Estimation in WiMAX Networks using Received Signal Strength." International Conference on Electrical Engineering 9, no. 9th (2014): 1–13. http://dx.doi.org/10.21608/iceeng.2014.30361.

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Ning, Chao, Rui Li, and Kejiong Li. "Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting." Wireless Personal Communications 89, no. 2 (2016): 365–84. http://dx.doi.org/10.1007/s11277-016-3270-4.

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Pedapolu, Pavan K., Pushkar Saraf, Pradeep Kumar, et al. "Regression Based Mobility Estimation Method Using Received Signal Strength." Wireless Personal Communications 101, no. 1 (2018): 359–74. http://dx.doi.org/10.1007/s11277-018-5692-7.

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TAKIZAWA, Y., P. DAVIS, M. KAWAI, H. IWAI, A. YAMAGUCHI, and S. OBANA. "Self-Organizing Location Estimation Method Using Received Signal Strength." IEICE Transactions on Communications E89-B, no. 10 (2006): 2687–95. http://dx.doi.org/10.1093/ietcom/e89-b.10.2687.

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Mott, John H., and Qingsong Ai. "Estimation of aircraft distances using transponder signal strength information." Cogent Engineering 5, no. 1 (2018): 1466619. http://dx.doi.org/10.1080/23311916.2018.1466619.

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Obeidat, Huthaifa, Ali A. S. Alabdullah, Nazar T. Ali, et al. "Local Average Signal Strength Estimation for Indoor Multipath Propagation." IEEE Access 7 (2019): 75166–76. http://dx.doi.org/10.1109/access.2019.2918178.

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Huang, He, and Bin Luo. "A Received Signal Strength Indication Adaptive Algorithm for Wireless Sensor Network." Applied Mechanics and Materials 273 (January 2013): 505–9. http://dx.doi.org/10.4028/www.scientific.net/amm.273.505.

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Indoor environments are complicated and changeable, and RSSI (Received Signal Strength Indication) observations have great randomness, so the classic RSSI estimation algorithm has poor results in indoor environments. To solve this problem, a RSSI adaptive estimation algorithm (RAE-IW) based on Kalman filtering algorithm is presented in this paper, which achieves exact RSSI estimation, and fast adapts to the change of environmental parameters. Simulation results show that RAE-IW has low complexity, performs better than classic estimation methods in indoor environments, and applies to indoor wir

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LEI, KIN FONG, SHIH-CHUNG CHENG, MING-YIH LEE, and WEN-YEN LIN. "MEASUREMENT AND ESTIMATION OF MUSCLE CONTRACTION STRENGTH USING MECHANOMYOGRAPHY BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (2013): 1350020. http://dx.doi.org/10.4015/s1016237213500208.

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Muscle contraction strength estimation using mechanomyographic (MMG) signal is typically calculated by the root mean square (RMS) amplitude. Raw MMG signal is processed by rectification, low-pass filtering, and mapping. In this work, beside RMS amplitude, another significant parameter of MMG signal, i.e. frequency variance (VAR), is introduced and used for constructing an algorithm for estimating the muscle contraction strength. Seven participants produced isometric contractions about the elbow while MMG signal and generated torque (resultant of muscle contraction strength) of biceps brachii w

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FUJII, Masahiro, Yuma HIROTA, Hiroyuki HATANO, Atsushi ITO, and Yu WATANABE. "Distance Estimation Based on Statistical Models of Received Signal Strength." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E99.A, no. 1 (2016): 199–203. http://dx.doi.org/10.1587/transfun.e99.a.199.

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Dissertations / Theses on the topic "Signal Strength Estimation"

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Wong, Hak Lim. "Signal strength-based location estimation in two different mobile networks." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/700.

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Winfree, Sean. "Angle of arrival estimation using received signal strength with directional antennas." Connect to resource, 2007. http://hdl.handle.net/1811/25093.

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Thesis (Honors)--Ohio State University, 2007.<br>Title from first page of PDF file. Document formatted into pages: contains 39 p.; also includes graphics. Includes bibliographical references (p. 20). Available online via Ohio State University's Knowledge Bank.

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Li, Kejiong. "Indoor and outdoor location estimation in large areas using received signal strength." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8537.

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Location estimation when deployed on wireless networks supports a range of services including user tracking and monitoring, health care support and push and pull marketing. The main subject of this thesis is improving indoor and outdoor location estimation accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or access points (APs), without using the global positioning system (GPS) or triangulation methods. For the outdoor environment, state-of-the-art deterministic and probabilistic algorithms are adapted to exploit principal components (PCs) and clustering. The

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Chitte, Sree Divya. "Source localization from received signal strength under lognormal shadowing." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/477.

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This thesis considers statistical issues in source localization from the received signal strength (RSS) measurements at sensor locations, under the practical assumption of log-normal shadowing. Distance information of source from sensor locations can be estimated from RSS measurements and many algorithms directly use powers of distances to localize the source, even though distance measurements are not directly available. The first part of the thesis considers the statistical analysis of distance estimation from RSS measurments. We show that the underlying problem is inefficient and there is on

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Holm, Rasmus. "Energy-Efficient Mobile Communication with Cached Signal Maps." Thesis, Linköpings universitet, Programvara och system, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-124607.

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Data communication over cellular networks is expensive for the mobile device in terms of energy, especially when the received signal strength (RSS) is low. The mobile device needs to amplify its transmission power to compensate for noise leading to an increased energy consumption. This thesis focuses on developing a RSS map for the third generation cellular technology (3G) which can be stored locally at the mobile device, and can be used for avoiding expensive communication in low RSS areas. The proposed signal map is created by crowdsourced information collected from several mobile devices. A

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Jeevarathnam, Nanda Gopal. "Grid-Based RFID Indoor Localization Using Tag Read Count and Received Signal Strength Measurements." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7039.

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Passive ultra-high frequency (UHF) radio frequency identification (RFID) systems have gained immense popularity in recent years for their wide-scale industrial applications in inventory tracking and management. In this study, we explore the potential of passive RFID systems for indoor localization by developing a grid-based experimental framework using two standard and easily measurable performance metrics: received signal strength indicator (RSSI) and tag read count (TRC). We create scenarios imitating real life challenges such as placing metal objects and other RFID tags

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Shuaieb, Wafa S. A. "Radio-Location Techniques for Localization and Monitoring Applications. A study of localisation techniques, using OFDM system under adverse channel conditions and radio frequency identification for object identification and movement tracking." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/18185.

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A wide range of services and applications become possible when accurate position information for a radio terminal is available. These include: location-based services; navigation; safety and security applications. The commercial, industrial and military value of radio-location is such that considerable research effort has been directed towards developing related technologies, using satellite, cellular or local area network infrastructures or stand-alone equipment. This work studies and investigates two location techniques. The first one presents an implementation scheme for a wide

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Morávek, Patrik. "Lokalizace v bezdrátových sítích s omezenými energetickými zdroji." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-233572.

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Tato disertační práce se věnuje lokalizaci v bezdrátových sítích se zaměřením na odhad vzdálenosti. Lokalizace je v bezdrátových sítích s mobilními ale i statickými uzly důležitým procesem, neboť znalost pozice uzlů může být během provozu sítě dále s výhodou využita. V práci je prezentována nová metoda odhadu vzdálenosti na základě měření síly přijatého signálu. Navržená metoda je postavena tak, aby s co nejnižšími energetickými náklady dosáhla požadovaného stupně přesnosti i ve značně odlišných rádiových podmínkách. Před návrhem vlastní metody byla provedena experimentální analýza spotřeby an

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Šimek, Milan. "Výběr referenčních uzlů pro bezkotevní lokalizační techniky v bezdrátových senzorových sítích." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-233511.

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Dizertační práce se zabývá návrhem nového bezkotevního lokalizačního algoritmu sloužícího pro výpočet pozice uzlů v bezdrátových senzorových sítích. Provedené studie ukázaly, že dosavadní bezkotevní lokalizační algoritmy, pracující v paralelním režimu, dosahují malých lokalizačních chyb. Jejich nevýhodou ovšem je, že při sestavení množiny referenčních uzlu spotřebovávají daleko větší množství energie než algoritmy pracující v inkrementálním režimu. Paralelní lokalizační algoritmy využívají pro určení pozice referenční uzly nacházející se na protilehlých hranách bezdrátové sítě. Nový lokalizačn

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Obeidat, Huthaifa A. N. "Investigation of Indoor Propagation Algorithms for Localization Purposes: Simulation and Measurements of Indoor Propagation Algorithms for Localization Applications using Wall Correction Factors, Local Mean Power Estimation and Ray Tracing Validations." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17385.

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The objective of this work is to enhance the awareness of the indoor propagation behaviour, by a set of investigations including simulations and measurements. These investigations include indoor propagation behaviour, local mean power estimation, proposing new indoor path loss model and introducing a case study on 60 GHz propagation in indoor environments using ray tracing and measurements. A summary of propagation mechanisms and manifestations in the indoor environment is presented. This comprises the indoor localization techniques using channel parameters in terms of angle of arrival (A

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Books on the topic "Signal Strength Estimation"

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Spera, David A. Equations for estimating the strength of TV signals scattered by wind turbines. Lewis Research Center, 1994.

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GPS Signal Offset Detection and Noise Strength Estimation in a Parallel Kalman Filter Algorithm. Storming Media, 1999.

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L, Sengupta Dipak, and Lewis Research Center, eds. Equations for estimating the strength of TV signals scattered by wind turbines. Lewis Research Center, 1994.

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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art

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Book chapters on the topic "Signal Strength Estimation"

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Penrose, J. D., and T. J. Pauly. "An Evaluation of Target Strength Estimation from Echo Ensembles." In Acoustic Signal Processing for Ocean Exploration. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1604-6_38.

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Noureddine, Hadi, Damien Castelain, and Ramesh Pyndiah. "Train Tracking and Shadowing Estimation Based on Received Signal Strength." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19786-4_3.

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Valadares, Dalton Cézane Gomes, Joseana Macêdo Fechine Régis de Araújo, Ângelo Perkusich, Marco Aurélio Spohn, Elmar Uwe Kurt Melcher, and Natália Porfírio Albuquerque. "Towards 802.11g Signal Strength Estimation in an Industrial Environment: A Practical Study." In Advanced Information Networking and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15032-7_47.

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Zhang, Minghua, and Shensheng Zhang. "An Accurate and Fast WLAN User Location Estimation Method Based on Received Signal Strength." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_8.

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Sieskul, Bamrung Tau, Feng Zheng, and Thomas Kaiser. "Mobile Position Estimation Using Received Signal Strength and Time of Arrival in Mixed LOS/NLOS Environments." In Handbook of Position Location. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118104750.ch18.

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Dwiyasa, Felis, Meng-Hiot Lim, Yew-Soon Ong, and Bijaya Panigrahi. "Equality Constrained-Optimization-Based Semi-supervised ELM for Modeling Signal Strength Temporal Variation in Indoor Location Estimation." In Proceedings of ELM-2015 Volume 1. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28397-5_30.

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Sharp, Ian, and Kegen Yu. "New Methods for Improved Indoor Signal Strength Positioning." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3528-7.ch001.

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This chapter focuses on positioning based on received signal strength measurements and radio path-loss modeling. Typically, receiver signal strength from a device such as a smart phone is used to calculate the path-loss and thus estimate distances using a loss model calibrated in an offline process. With known positions and ranges to WiFi access points or simple devices using Bluetooth or Zigbee for data communications, the mobile device position can be estimated. However, due to the complex multipath propagation indoor environment, distance estimation and position determination using current methods are not very accurate. Based on knowledge of the nature of indoor signal propagation and algorithms especially designed for mobile applications, new methods show that positional accuracy of a few meters is possible, even with non-line-of-sight propagation through many intervening walls. Given the current widespread deployment of WiFi indoors, simple software-only solutions are feasible for applications such as general personal navigation and tracking within buildings.

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Chen, Liang, Heidi Kuusniemi, Yuwei Chen, et al. "Information Filter-Assisted Indoor Bluetooth Positioning." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6034-2.ch007.

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This chapter studies wireless positioning using a network of Bluetooth signals. Fingerprints of Received Signal Strength Indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors applied an information filter method with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is assisted to correct the outliers of position estimation. The field tests show the effectiveness of the information filter-assisted positioning method, which improves the horizontal positioning accuracy of indoor navigation by about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter. In RSSI fingerprinting localization, building a fingerprint database is usually time-consuming and labour-intensive. In the final section, a self-designed autonomous SLAM robot platform is introduced to be able to carry out the Bluetooth RSS data collecting.

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Patwari, Neal, and Piyush Agrawal. "Calibration and Measurement of Signal Strength for Sensor Localization." In Localization Algorithms and Strategies for Wireless Sensor Networks. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch005.

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A number of practical issues are involved in the use of measured received signal strength (RSS) for purposes of localization. This chapter focuses on device effects and modeling problems which are not well covered in the literature, such as transceiver device manufacturing variations, battery effects on transmit power, nonlinearities in RSSI circuits, and path loss model parameter estimation. The authors discuss both the negative impacts of these effects and inaccuracies, and adaptations used by particular localization algorithms to be robust to them, without discussing any algorithm in detail. The authors present measurement methodologies to characterize these effects for wireless sensor nodes, and report the results from several calibration experiments to quantify each discussed effect and modeling issue.

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Guo, Cheng, R. Venkatesha Prasad, Jing Wang, Vijay Sathyanarayana Rao, and Ignas Niemegeers. "Localizing Persons Using Body Area Sensor Network." In Developments in Wireless Network Prototyping, Design, and Deployment. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1797-1.ch013.

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Context awareness is an important aspect in many ICT applications. For example, in an intelligent home network, location of the user enables session transfer, lighting, and temperature control, et cetera. In fact, in a body area sensor network (BASN), location estimation of a user helps in realizing realtime monitoring of the person (especially those who require help) for better health supervision. In this chapter the authors first introduce many localization methods and algorithms from the literature in BASNs. They also present classification of these methods. Amongst them, location estimation using signal strength is one of the foremost. In indoor environments, the authors found that the signal strength based localization methods are usually not accurate, since signal strength fluctuates. The fluctuation in signal strength is due to deficient antenna coverage and multi-path interference. Thus, localization algorithms usually fail to achieve good accuracy. The authors propose to solve this problem by combining multiple receivers in a body area sensor network to estimate the location with a higher accuracy. This method mitigates the errors caused by antenna orientations and beam forming properties. The chapter evaluates the performance of the solution with experiments. It is tested with both range-based and range-free localization algorithm that we developed. The chapter shows that with spatial diversity, the localization accuracy is improved compared to using single receiver alone. Moreover, the authors observe that range-based algorithm has a better performance.

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Conference papers on the topic "Signal Strength Estimation"

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Patwari, Neal, and Alfred O. Hero. "Demonstrating distributed signal strength location estimation." In the 4th international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1182807.1182851.

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Mostofi, Yasamin, Mehrzad Malmirchegini, and Alireza Ghaffarkhah. "Estimation of communication signal strength in robotic networks." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509677.

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Yu, Paul L., and Brian M. Sadler. "Received signal strength gradient estimation for mobile networks." In MILCOM 2010 - 2010 IEEE Military Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/milcom.2010.5680357.

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Ferris, B., D. Haehnel, and D. Fox. "Gaussian Processes for Signal Strength-Based Location Estimation." In Robotics: Science and Systems 2006. Robotics: Science and Systems Foundation, 2006. http://dx.doi.org/10.15607/rss.2006.ii.039.

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Santos, Irene, and Petar M. Djuric. "Crowdsource-based signal strength field estimation by Gaussian processes." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081401.

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Waadt, Andreas E., Christian Kocks, Shangbo Wang, Guido H. Bruck, and Peter Jung. "Maximum likelihood localization estimation based on received signal strength." In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010). IEEE, 2010. http://dx.doi.org/10.1109/isabel.2010.5702817.

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Brahmbhatt, Sachi, Aishwary Kulshrestha, and Gaurav Singal. "SSLSM: Signal Strength Based Link Stability Estimation in MANETs." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.41.

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Jain, Vinod Kumar, Shashikala Tapaswi, and Anupam Shukla. "Distributed location estimation system using WLAN received signal strength fingerprints." In 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012. http://dx.doi.org/10.1109/wcnc.2012.6214338.

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Chen, Qi, Gaoming Huang, and Shiqiong Song. "WLAN User Location Estimation Based on Receiving Signal Strength Indicator." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5305128.

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Alotaibi, Majid, Konstanty S. Bialkowski, and Adam Postula. "A signal strength based tag estimation technique for RFID systems." In 2010 IEEE International Conference on Rfid-Technology and Applications (RFID-TA). IEEE, 2010. http://dx.doi.org/10.1109/rfid-ta.2010.5529929.

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Reports on the topic "Signal Strength Estimation"

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Liaw, Y. P., D. R. Cook, and D. L. Sisterson. Estimation of lightning stroke peak current as a function of peak electric field and the normalized amplitude of signal strength: Corrections and improvements. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10174645.

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