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Relevant bibliographies by topics / NLOS

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Academic literature on the topic 'NLOS'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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Journal articles on the topic "NLOS"

1

Cheng, Long, Sihang Huang, Mingkun Xue, and Yangyang Bi. "A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network." Sensors 20, no. 22 (November 19, 2020): 6634. http://dx.doi.org/10.3390/s20226634.

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With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.

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Zhang, Hao, Qing Wang, Chao Yan, Jiujing Xu, and Bo Zhang. "Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS." Remote Sensing 14, no. 24 (December 14, 2022): 6338. http://dx.doi.org/10.3390/rs14246338.

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Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

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Liu, Ang, Shiwei Lin, Jianguo Wang, and Xiaoying Kong. "A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System." Sensors 22, no. 21 (October 27, 2022): 8247. http://dx.doi.org/10.3390/s22218247.

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Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.

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Cheng, Long, Yifan Li, Yan Wang, Yangyang Bi, Liang Feng, and Mingkun Xue. "A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks." Sensors 19, no. 5 (March 10, 2019): 1215. http://dx.doi.org/10.3390/s19051215.

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With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.

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Hao, Yukai, and Xin Qiu. "Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm." Complexity 2021 (February 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/6655889.

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In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network (ANN) is proposed for NLOS positioning error caused by non-line-of-sight (NLOS) propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training algorithm based on extended Kalman (EKF). Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy.

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Xu, Yan Ying, Song Jian Bao, and Yu Lin Wang. "Analysis and Research of Mobile Station Location Based on NLOS Error." Applied Mechanics and Materials 713-715 (January 2015): 1460–64. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1460.

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Existed in the work of wireless positioning error, the need to suppress NLOS (Non line of sight) transmission problem of positioning the bad influence of the NLOS system model is put forward and the novel geometric positioning model, the introduction of appropriate NLOS channels model to suppress NLOS error, and make full use of the propagation characteristics of derived meet MS (Mobile Station) coordinates equation, with two NLOS paths can only calculate the position of MS, and using only a single base Station can complete the MS positioning, overcome the base Station number too little to pinpoint the flaws of the MS. This paper also gives a method of least squares and maximum likelihood algorithm, using the NLOS paths to improve the positioning accuracy. So as to realize the movement of the MS in NLOS environment position tracking. Through the theoretical analysis and computer simulation analysis, the results show that the positioning method in NLOS environment on the effectiveness and accuracy of the MS positioning.

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Kan, Ruixiang, Mei Wang, Zou Zhou, Peng Zhang, and Hongbing Qiu. "Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–20. http://dx.doi.org/10.1155/2022/5210388.

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The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.

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Yu, Xiaosheng, Peng Ji, Ying Wang, and Hao Chu. "Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network." Journal of Sensors 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5325174.

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Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.

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Long, Shan, Zhe Cui, and Fei Song. "A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment." Applied Mechanics and Materials 347-350 (August 2013): 3604–8. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3604.

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Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.

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Song, Bo, Sheng-Lin Li, Mian Tan, and Qing-Hui Ren. "A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2018 (December 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/1580147.

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Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB). In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years. Therefore, the identification of NLOS signals is of great significance in UWB positioning. The traditional machine learning method is based on the assumption that the number of samples of the line-of-sight (LOS) and NLOS signals are balanced. However, in reality, the number of LOS signals in UWB positioning is much larger than the NLOS signals. So the samples are characterized by class-imbalance. In response to this fact, we applied a fast imbalanced binary classification method based on moments (MIBC) to identify NLOS signals. The method uses the mean and covariance of the two first moments of the LOS signal samples to represent its probability distribution and then uses the probability distribution and all a small amount of NLOS signal samples to establish a model. This method does not depend on the number of LOS signals and is suitable for dealing with the problem of classification of the imbalance between the number of LOS and NLOS signals. Numerical simulations also verify that the method has better performance than LS-SVM and SVDD.

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Dissertations / Theses on the topic "NLOS"

1

Ko, Edwin Hoi-Kit. "NLOS mobile location determination in CDMA cellular systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0001/MQ45990.pdf.

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Al-Jazzar, Saleh. "Algorithms and Parameter Estimation for Radiolocation in NLOS Environments." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1084913001.

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Tau, Sieskul Bamrung [Verfasser]. "NLoS Localization and UWB Channel Capacity Analysis / Bamrung Tau Sieskul." Aachen : Shaker, 2010. http://d-nb.info/1080766995/34.

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Kbayer, Nabil. "Advanced Signal Processing Methods for GNSS Positioning with NLOS/Multipath Signals." Thesis, Toulouse, ISAE, 2018. http://www.theses.fr/2018ESAE0017/document.

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Les avancées récentes dans le domaine de navigation par satellites (GNSS) ontconduit à une prolifération des applications de géolocalisation dans les milieux urbains. Pourde tels environnements, les applications GNSS souffrent d’une grande dégradation liée à laréception des signaux satellitaires en lignes indirectes (NLOS) et en multitrajets (MP). Cetravail de thèse propose une méthodologie originale pour l’utilisation constructive des signauxdégradés MP/NLOS, en appliquant des techniques avancées de traitement du signal ou àl’aide d’une assistance d’un simulateur 3D de propagation des signaux GNSS. D’abord, nousavons établi le niveau maximal réalisable sur la précision de positionnement par un systèmeGNSS "Stand-Alone" en présence de conditions MP/NLOS, en étudiant les bornes inférieuressur l’estimation en présence des signaux MP/NLOS. Pour mieux améliorer ce niveau deprécision, nous avons proposé de compenser les erreurs NLOS en utilisant un simulateur 3D dessignaux GNSS afin de prédire les biais MP/NLOS et de les intégrer comme des observationsdans l’estimation de la position, soit par correction des mesures dégradées ou par sélectiond’une position parmi une grille de positions candidates. L’application des approches proposéesdans un environnement urbain profond montre une bonne amélioration des performances depositionnement dans ces conditions
Recent trends in Global Navigation Satellite System (GNSS) applications inurban environments have led to a proliferation of studies in this field that seek to mitigatethe adverse effect of non-line-of-sight (NLOS). For such harsh urban settings, this dissertationproposes an original methodology for constructive use of degraded MP/NLOS signals, insteadof their elimination, by applying advanced signal processing techniques or by using additionalinformation from a 3D GNSS simulator. First, we studied different signal processing frameworks,namely robust estimation and regularized estimation, to tackle this GNSS problemwithout using an external information. Then, we have established the maximum achievablelevel (lower bounds) of GNSS Stand-Alone positioning accuracy in presence of MP/NLOSconditions. To better enhance this accuracy level, we have proposed to compensate for theMP/NLOS errors using a 3D GNSS signal propagation simulator to predict the biases andintegrate them as observations in the estimation method. This could be either by correctingdegraded measurements or by scoring an array of candidate positions. Besides, new metricson the maximum acceptable errors on MP/NLOS errors predictions, using GNSS simulations,have been established. Experiment results using real GNSS data in a deep urban environmentshow that using these additional information provides good positioning performance enhancement,despite the intensive computational load of 3D GNSS simulation

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Dammes, Timo [Verfasser]. "Probabilistic Range Estimation for Wireless Indoor Localization in NLOS Scenarios / Timo Dammes." Aachen : Shaker, 2015. http://d-nb.info/1069050407/34.

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Li, Hao. "New mobile positioning techniques for LOS/NLOS environments and investigation of topology influence." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/6007/.

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The advent of wireless location technology and the increase in location-based services, has meant the need to investigate efficient network-based location methods becoming of paramount importance. Therefore, the interest in wireless positioning techniques has been increasing over recent decades. Among mobile positioning techniques, the Time of Arrival (TOA) and Time Difference of Arrival (TDOA) look promising. For the purpose of dealing with such technologies, some classic algorithms such as least square, most likelihood and Taylor method have been used to solve the estimation, which distinguishes the location. However, in real practice, there are certain factors that influence the level of location accuracy. The two most significant factors are cellular topologies and non-line-of-sight (NLOS) effect. This thesis reviews existing approaches and suggests innovative methods for both line-of-sight (LOS) and NLOS scenarios. A simulation platform is designed to test and compare the performances of these algorithms. The results of the simulation compared with actual position measurements demonstrate that the innovative approaches have high positioning accuracy. Additionally, this thesis demonstrates different types of cellular topologies and develops a simulation to show how the cellular topology affects the positioning quality level. Finally, this thesis implements an experiment to exhibit how the innovative algorithms perform in the real world.

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Li, Binghao Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "Terrestial mobile user positioning using TDOA and fingerprinting techniques." Awarded by:University of New South Wales. School of Surveying and Spatial Information Systems, 2006. http://handle.unsw.edu.au/1959.4/25507.

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Specialists are expecting the knowledge of location will trigger yet another revolution in mobile services. Location-base services (LBS) have attracted many researchers and enterprises and one of the key aspects of LBS is positioning technology. Considering a wider and more complex field - ubiquitous computing, location is the fundamental element. This work focuses on some aspects of the new techniques of terrestrial positioning systems. Wireless LAN is one of the most popular systems used for positioning for indoor environments and public places. We have investigated the trilateration and fingerprinting approaches and the results showed the advantages of fingerprinting. A novel method to generate the fingerprints database based on Universal Kriging (UK) was developed, which can not only significantly decrease the training time, but also increase the accuracy of estimates. In mobile phone positioning systems, most techniques suffer from the non-line-of-sight (NLOS) propagation. We investigated the specifics of NLOS error, and proposed a method to mitigate the errors. Furthermore, a new algorithm named WSMM (wireless signal map matching) was discussed. Simulations and experiments verified the idea, and the accuracy of positioning can be improved greatly. Since fingerprinting technique can utilize rather than suffer from the NLOS propagation, it was also applied in mobile phone positioning system. Experiments showed both the deterministic approach and probabilistic approach can provide better results comparing with other techniques in suburban area. To achieve a robust positioning system and provide more useful information of the user, multisensor combination and data fusion are necessary. As the first step of future research, a mulitsensor synchronization system was developed. This system can promisingly achieve synchronization with error less than 0.4 ms, which is suitable for most land applications. Hence the main findings of this thesis are: (1) a novel method of yielding fingerprint database for both wireless LAN (WLAN) and mobile phone systems when using the fingerprinting technique for positioning; (2) a database method to mitigate NLOS error for mobile phone positioning systems; (3) a low cost synchronization system for integration of multiple sensors.

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Venkatraman, Saipradeep. "Wireless Location in Non-Line-of-Sight Environments." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085685908.

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Olayanju, Iyeyinka Damilola, and Olabode Paul Ojelabi. "Using Multilateration and Extended Kalman Filter for Localization of RFID Passive Tag in NLOS." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1119.

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The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especially in complicated propagation environment due to the nature and features of the environment. There are different kinds of localisation systems and they are Global Positioning System (GPS) which can be used for accurate outdoor localization; while technologies like artificial vision, ultrasonic signals, infrared and radio frequency signals can be employed for indoor localization. This project focuses on the location estimation in RFID Non Line-of-Sight (NLOS) environment using Real Time Localization System (RTLS) with passive tags, in carrying out passengers and baggage tracking at the airport. Indoor location radio sensing suffers from reflection, refraction and diffractions due to the nature of the environment. This unfavourable phenomenon called multipath leads to delay in the arrival of signal and the strength of signal received by receiving antenna within the propagation channel which in turns affects the RSSI, yielding inaccurate location estimation. RTLS based on time difference of arrival and error compensation technique and extended Kalman filter technique were employed in a NLOS environment to determine the location of tag. The better method for location estimation in a NLOS between the Kalman filtering and extended Kalman filtering is investigated. According to simulation results, the extended Kalman filtering technique is more suitable to be applied to RTLS.

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McCoy, Edward D. "Employment and command and control for the Non-Line-of-Sight (NLOS) missile system." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Jun/09Jun%5FNAME.pdf.

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Thesis (M.S. in Systems Technology (Command Control and Communications))--Naval Postgraduate School, June 1990.
Thesis Advisor(s): Caldwell, William J. "June 1990." Description based on title screen as viewed on October 16, 2009. Author(s) subject terms: NLOS Weapon System Description, NLOS Organization and Employment, NLOS Command and Control, NLOS Command and Control Testing. Includes bibliographical references (p. 63).

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Books on the topic "NLOS"

1

Ko, Edwin Hoi-Kit. NLOS mobile location determination in CDMA cellular systems. Ottawa: National Library of Canada, 1999.

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McCoy, Edward D. Employment and command and control for the Non-Line-of-Sight (NLOS) missile system. Monterey, California: Naval Postgraduate School, 1990.

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3

Kozyrev, Vi︠a︡cheslav. Rukotvornye NLO. Moskva: "I︠A︡UZA", 2005.

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Azhazha, V. G. Podvodnye NLO. Moskva: Veche, 2008.

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Zubchenko, Mikhail. Piloty NLO. Moskav: Putʹ, 1994.

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Gershteĭn, Mikhail. Taĭny krusheniĭ NLO. Moskva: AST, 2007.

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Gershteĭn, Mikhail. Taĭny krusheniĭ NLO. Moskva: AST, 2007.

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I͡Akubovskiĭ, Ėduard. NLO vokrug nas. Ekaterinburg: Sredne-Uralʹskoe knizhnoe izd-vo, 1992.

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Chernobrov, V. A. Ėnt︠s︡iklopedii︠a︡ vizitov NLO. Moskva: Veche, 2008.

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Vladimir, Zabelyshenskiĭ, ed. Fenomen NLO: Argumenty ufologii. Moskva: Ripol klassik, 2006.

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Book chapters on the topic "NLOS"

1

Liu, Deliang, Yi Yao, and You Zhai. "TOA Localization in NLOS Environments." In Lecture Notes in Electrical Engineering, 495–503. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3229-5_52.

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Gentile, Camillo, Nayef Alsindi, Ronald Raulefs, and Carole Teolis. "Multipath and NLOS Mitigation Algorithms." In Geolocation Techniques, 59–97. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1836-8_3.

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Sharp, Ian, and Kegen Yu. "NLOS Mitigation for Vehicle Tracking." In Navigation: Science and Technology, 505–30. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8791-2_16.

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Lie, Joni Polili, Chin-Heng Lim, and Chong-Meng Samson See. "NLOS Mitigation Methods for Geolocation." In Handbook of Position Location, 557–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118104750.ch17.

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Zhu, Xuanzhang. "A Novel NLOS Mitigation Algorithm Using VBS." In Lecture Notes in Electrical Engineering, 209–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40633-1_27.

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Xu, Wenjie, Zhonghai Wang, and Seyed A. Reza Zekavat. "An Introduction to NLOS Identification and Localization." In Handbook of Position Location, 523–55. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118104750.ch16.

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Kaiser, Thomas, Christiane Senger, Amr Eltaher, and Bamrung Tau Sieskul. "Localisation in NLOS Scenarios with UWB Antenna Arrays." In Ultra-Wideband, 389–411. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470056843.ch18.

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Bao, Dazhi, Hao Zhou, Hao Chen, Shaojie Liu, Yifan Zhang, and Zhiyong Feng. "TOA Based Localization Under NLOS in Cognitive Radio Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 668–79. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40352-6_55.

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Sun, Yinghao, Gang Wang, and Youming Li. "Robust RSS-Based Localization in Mixed LOS/NLOS Environments." In Communications and Networking, 659–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41114-5_49.

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Guimarães, A. G., and M. A. Grivet. "Identification of LOS/NLOS States Using TOA Filtered Estimates." In Telecommunications and Networking - ICT 2004, 1067–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27824-5_139.

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Conference papers on the topic "NLOS"

1

Sorrentino, A., F. Nunziata, G. Ferrara, and M. Migliaccio. "An effective indicator for NLOS, nLOS, LOS propagation channels conditions." In 2012 6th European Conference on Antennas and Propagation (EuCAP). IEEE, 2012. http://dx.doi.org/10.1109/eucap.2012.6206269.

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Bocquet, M., Ch Loyez, and A. Benlarbi-Delai. "Weak NLOS Channel Sounding." In 2006 European Microwave Conference. IEEE, 2006. http://dx.doi.org/10.1109/eumc.2006.280999.

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Mohan, Deepika, G. G. Md Nawaz Ali, and Peter Han Joo Chong. "Machine Learning Algorithm for NLOS Millimeter Wave in 5G V2X Communication." In 8th International Conference on Computational Science and Engineering (CSE 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101706.

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The 5G vehicle-to-everything (V2X) communication for autonomous and semi-autonomous driving utilizes the wireless technology for communication and the Millimeter Wave bands are widely implemented in this kind of vehicular network application. The main purpose of this paper is to broadcast the messages from the mmWave Base Station to vehicles at LOS (Line-ofsight) and NLOS (Non-LOS). Relay using Machine Learning (RML) algorithm is formulated to train the mmBS for identifying the blockages within its coverage area and broadcast the messages to the vehicles at NLOS using a LOS nodes as a relay. The transmission of information is faster with higher throughput and it covers a wider bandwidth which is reused, therefore when performing machine learning within the coverage area of mmBS most of the vehicles in NLOS can be benefited. A unique method of relay mechanism combined with machine learning is proposed to communicate with mobile nodes at NLOS.

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Triki, Mahdi, and Dirk T. M. Slock. "Mobile Localization for NLOS Propagation." In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2007. http://dx.doi.org/10.1109/pimrc.2007.4394714.

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Fujita, Takahiro, and Tomoaki Ohtsuki. "Low Complexity Localization Algorithm Based on NLOS Node Identification Using Minimum Subset for NLOS Environments." In IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. IEEE, 2008. http://dx.doi.org/10.1109/glocom.2008.ecp.1029.

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Jiang, Hao, Jie Xu, and Zhen Li. "NLOS Mitigation Method for TDOA Measurement." In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2010. http://dx.doi.org/10.1109/iihmsp.2010.56.

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Yu, Kegen, and Y. Jay Guo. "Efficient Location Estimators in NLOS Environments." In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2007. http://dx.doi.org/10.1109/pimrc.2007.4394428.

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Gupta, Chirag, and Deepanjan Biswas. "Pedestrian tracking algorithm in NLOS environments." In 2012 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS). IEEE, 2012. http://dx.doi.org/10.1109/ants.2012.6524231.

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Jiang, Wuyang, and Baogang Ding. "TDOA Localization Scheme with NLOS Mitigation." In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-fall49728.2020.9348443.

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Jia, Tao, and R. Michael Buehrer. "Collaborative position location with NLOS mitigation." In 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops). IEEE, 2010. http://dx.doi.org/10.1109/pimrcw.2010.5670376.

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Reports on the topic "NLOS"

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Hemenway, Mark, and Ronald H. Lafond. Non-Line of Sight-Combined Arms (NLOS-CA) Manpower, Personnel and Logistics Impact Analysis (LIA). Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada274201.

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Emis, Jonathon, Bryan Huang, Timothy Jones, Mei Li, and Don Tumbocon. Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465615.

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Hemenway, Mark, and Ronald H. Lafond. Non-Line of Sight - Combined Army (NLOS-CA) Manpower, Personnel and Logistics Impact Analyses (LIA) - Abstract. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada274164.

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Pack, D. J., and D. J. Downing. NLO error propagation exercise: statistical results. Office of Scientific and Technical Information (OSTI), September 1985. http://dx.doi.org/10.2172/5053984.

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Alfano, James, Isaac Barash, Thomas Clemente, Paul E. Staswick, Guido Sessa, and Shulamit Manulis. Elucidating the Functions of Type III Effectors from Necrogenic and Tumorigenic Bacterial Pathogens. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592638.bard.

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Many phytopathogenic bacteria use a type III protein secretion system (T3SS) to inject type III effectors into plant cells. In the experiments supported by this one-year feasibility study we investigated type III effector function in plants by using two contrasting bacterial pathogens: Pseudomonas syringae pv. tomato, a necrotrophic pathogen and Pantoea agglomerans, a tumorigenic pathogen. The objectives are listed below along with our major conclusions, achievements, and implications for science and agriculture. Objective 1: Compare Pseudomonas syringae and Pantoea agglomerans type III effectors in established assays to test the extent that they can suppress innate immunity and incite tumorigenesis. We tested P. agglomerans type III effectors in several innate immunity suppression assays and in several instances these effectors were capable of suppressing plant immunity, outputs that are suppressed by P. syringae effectors. Interestingly, several P. syringae effectors were able to complement gall production to a P. agglomerans pthGmutant. These results suggest that even though the disease symptoms of these pathogens are dramatically different, their type III effectors may function similarly. Objective 2: Construct P. syringae mutants in different combinations of type III-related DNA clusters to reduce type III effector redundancy. To determine their involvement in pathogenicity we constructed mutants that lack individual and multiple type III-related DNA clusters using a Flprecombinase-mediated mutagenesis strategy. The majority of single effector mutants in DC3000 have weak pathogenicity phenotypes most likely due to functional redundancy of effectors. Supporting this idea, Poly-DNAcluster deletion mutants were more significantly reduced in their ability to cause disease. Because these mutants have less functional redundancy of type III effectors, they should help identify P. syringae and P. agglomerans effectors that contribute more significantly to virulence. Objective 3: Determine the extent that P. syringae and P. agglomerans type III effectors alter hormone levels in plants. Inhibition of auxin polar transport by 2,3,5-triiodobenzoic acid (TIBA) completely prevented gall formation by P. agglomerans pv. gypsophilae in gypsophila cuttings. This result supported the hypothesis that auxin and presumably cytokinins of plant origin, rather than the IAA and cytokinins secreted by the pathogen, are mandatory for gall formation. Transgenic tobacco with pthGshowed various phenotypic traits that suggest manipulation of auxin metabolism. Moreover, the auxin levels in pthGtransgenic tobacco lines was 2-4 times higher than the control plants. External addition of auxin or cytokinins could modify the gall size in gypsophila cuttings inoculated with pthGmutant (PagMx27), but not with other type III effectors. We are currently determining hormone levels in transgenic plants expressing different type III effectors. Objective 4: Determine whether the P. agglomerans effectors HsvG/B act as transcriptional activators in plants. The P. agglomerans type III effectors HsvG and HsvB localize to the nucleus of host and nonhost plants and act as transcription activators in yeast. Three sites of adjacent arginine and lysine in HsvG and HsvB were suspected to act as Nuclear localization signals (NLS) domains. A nuclear import assay indicated two of the three putative NLS domains were functional NLSs in yeast. These were shown to be active in plants by fusing HsvG and HsvB to YFP. localization to the nucleus was dependent on these NLS domains. These achievements indicate that our research plan is feasible and suggest that type III effectors suppress innate immunity and modulate plant hormones. This information has the potential to be exploited to improve disease resistance in agricultural crops.

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Soni, Som R., J. B. Skidmore, Rajesh Tiwari, R. M. McKellar, and Perry P. Yancey. Nonlinear Optical (NLO) Materials and Device Development. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada380784.

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Brandelik, Joseph. Nonlinear Optical (NLO) Polymer Opto-Electronic Devices. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada384482.

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Anastasiou, Charalampos. NLO Higgs boson rapidity distributions at hadron colliders. Office of Scientific and Technical Information (OSTI), November 2002. http://dx.doi.org/10.2172/808704.

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Mandal, B. K., T. Takahsshi, M. Maeda, S. Kumar, and A. Blumstein. Comb-Like Polymers Containing NLO active Pendant Groups. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada226514.

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Wright, Michael E., and Edward G. Toplikar. Organometallic Nonlinear Optical (NLO) Polymers. 2. Synthesis of Main-Chain Organometallic Polymers and a Structural Study of Ferrocene NLO-Phores. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada250655.

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