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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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Suzuki, Taro, and Yoshiharu Amano. "NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning." Sensors 21, no. 7 (April 3, 2021): 2503. http://dx.doi.org/10.3390/s21072503.
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This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.
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Wang, Yan, Xuehan Wu, and Long Cheng. "A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks." Journal of Sensors 2018 (September 27, 2018): 1–10. http://dx.doi.org/10.1155/2018/3715372.
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The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.
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Wang, Lei, Ruizhi Chen, Lili Shen, Haiyang Qiu, Ming Li, Peng Zhang, and Yuanjin Pan. "NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm." Remote Sensing 11, no. 7 (March 31, 2019): 773. http://dx.doi.org/10.3390/rs11070773.
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The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.
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Tian, Shiwei, Luwen Zhao, and Guangxia Li. "A Support Vector Data Description Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/963418.
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Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.
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Janakiraman, Sengathir. "An improved rank criterion-based NLOS node detection mechanism in VANETs." International Journal of Intelligent Unmanned Systems 9, no. 1 (July 16, 2020): 1–15. http://dx.doi.org/10.1108/ijius-12-2019-0072.
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PurposeAn Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.Design/methodology/approachThis proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that are facing NLOS situations with the influence of channel contention and broadcast storm. It is developed for increasing the warning packet delivery rate with minimized overhead, delay and channel utilization.FindingsThe simulation results of the proposed IRC-NLOS-DM scheme confirmed the excellence of the proposed IRC-NLOS-DM over the existing works investigated based on the channel utilization rate, neighborhood prediction rate and emergency message forwarding rate.Practical implicationsIt is proposed for reliable warning message delivery in Vehicular Ad hoc Networks (VANETs) which is referred as the specialized category of mobile ad hoc network application that influences Intelligent Transportation Systems (ITS) and wireless communications. It is proposed for implementing vehicle safety applications for constructing a least cluttered and a secure environment on the road.Originality/valueIt is contributed as a significant mechanism for facilitating reliable dissemination of emergency messages between the vehicular nodes, which is essential in the critical environment to facilitate a risk-free environment. It also aids in creating a reliable environment for accurate localization of Non-Line of Sight (NLOS) nodes that intentionally introduces the issues of broadcasting storm and channel congestion during the process of emergency message exchanges.
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He, Chengwen, Yunbin Yuan, and Bingfeng Tan. "Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments." Sensors 21, no. 4 (February 13, 2021): 1321. http://dx.doi.org/10.3390/s21041321.
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Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy.
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Cheng, Long, Mingkun Xue, Ze Liu, and Yong Wang. "A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network." Applied Sciences 10, no. 1 (December 18, 2019): 6. http://dx.doi.org/10.3390/app10010006.
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As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.
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Wang, Yan, Yang Yan, Zhengjian Li, and Long Cheng. "A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network." Journal of Sensors 2020 (January 7, 2020): 1–17. http://dx.doi.org/10.1155/2020/6787252.
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The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.
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Wan, Pengwu, Jian Wei, Jin Wang, and Qiongdan Huang. "Wireless Sensor Network-Based Rigid Body Localization for NLOS Parameter Estimation." Sensors 22, no. 18 (September 8, 2022): 6810. http://dx.doi.org/10.3390/s22186810.
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In wireless sensor network (WSN)-based rigid body localization (RBL) systems, the non-line-of-sight (NLOS) propagation of the wireless signals leads to severe performance deterioration. This paper focuses on the RBL problem under the NLOS environment based on the time of arrival (TOA) measurement between the sensors fixed on the rigid body and the anchors, where the NLOS parameters are estimated to improve the RBL performance. Without any prior information about the NLOS environment, the highly non-linear and non-convex RBL problem is transformed into a difference of convex (DC) programming, which can be solved by using the concave–convex procedure (CCCP) to determine the position of the rigid body sensors and the NLOS parameters. To avoid error accumulation, the obtained NLOS parameters are utilized to refine the localization performance of the rigid body sensors. Then, the accurate position and the orientation of the rigid body in two-Dimensional space are obtained according to the relative deflection angle method. To reduce the computational complexity, the singular value decomposition (SVD) method is employed to solve the problem in three-Dimensional space. Simulation results show that the proposed method can effectively improve the performance of the rigid body localization based on the wireless sensor network in NLOS environment.
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Wang, Yan, Yang Cheng, and Long Cheng. "Fusion Localization Algorithm Based on Robust IMM Model Combined with Semi-Definite Programming." Actuators 11, no. 6 (May 29, 2022): 146. http://dx.doi.org/10.3390/act11060146.
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With the continuous development of wireless sensor network (WSN) technology, WSN has gradually become one of the key technologies of the Internet, and is widely used in indoor target location technology. However, the obstacles will have a great influence on the distance measurement, and it will result in a large positioning error. Therefore, how to deal with the non-line-of-sight (NLOS) error becomes an important problem. In this paper, Interacting Multiple Model (IMM) was used to identify NOLS/LOS. The NLOS probability was calculated by Markov transform probability, and the likelihood function was calculated by extended Kalman filter (EKF). The NLOS probability was compared with the LOS probability to judge whether the measurement contained the NLOS error. A robust algorithm combining IMM model with semidefinite programming (IMM-SDP) was proposed. The improved convex programming algorithm was proposed to reduce the NLOS error. Simulation and experimental results showed that the proposed algorithm can effectively reduce the influence of NLOS error and achieve higher positioning accuracy compared with the existing positioning methods.
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Tiwari, Smita, Donglin Wang, Michel Fattouche, and Fadhel Ghannouchi. "A Hybrid RSS/TOA Method for 3D Positioning in an Indoor Environment." ISRN Signal Processing 2012 (March 1, 2012): 1–9. http://dx.doi.org/10.5402/2012/503707.
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This paper investigates 3D positioning in an indoor line of sight (LOS) and nonline of sight (NLOS) combined environment. It is a known fact that time-of-arrival-(TOA-) based positioning outperforms other techniques in LOS environments; however, multipath in an indoor environment, especially NLOS multipath, significantly decreases the accuracy of TOA positioning. On the other hand, received-signal-strength-(RSS-) based positioning is not affected so much by NLOS multipath as long as the propagation attenuation can be correctly estimated and the multipath effects have been compensated for. Based on this fact, a hybrid weighted least square (HWLS) RSS/TOA method is proposed for target positioning in an indoor LOS/NLOS environment. The identification of LOS/NLOS path is implemented by using Nakagami distribution. An experiment is conducted in the iRadio lab, in the ICT building at the University of Calgary, in order to (i) demonstrate the availability of Nakagami distribution for the identification of LOS and NLOS path, (ii) estimate the pass loss exponent for RSS technique, and (iii) verify our proposed scheme.
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Chen, Jiajing, Xuefeng Yin, Li Tian, Nan Zhang, Yongyu He, Xiang Cheng, Weiming Duan, and Silvia Ruiz Boqué. "Measurement-Based LoS/NLoS Channel Modeling for Hot-Spot Urban Scenarios in UMTS Networks." International Journal of Antennas and Propagation 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/454976.
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A measurement campaign is introduced for modeling radio channels with either line-of-sight (LoS) or non-line-of-sight (NLoS) connection between user equipment (UE) and NodeB (NB) in an operating universal mobile telecommunications system. A space-alternating generalized expectation-maximization (SAGE) algorithm is applied to estimate the delays and the complex attenuations of multipath components from the obtained channel impulse responses. Based on a novel LoS detection method of multipath parameter estimates, channels are classified into LoS and NLoS categories. Deterministic models which are named “channel maps” and fading statistical models have been constructed for LoS and NLoS, respectively. In addition, statistics of new parameters, such as the distance between the NB and the UE in LoS/NLoS scenarios, the life-distance of LoS channel, the LoS existence probability per location and per NB, the power variation at LoS to NLoS transition and vice versa, and the transition duration, are extracted. These models are applicable for designing and performance evaluation of transmission techniques or systems used by distinguishing the LoS and NLoS channels.
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Zhang, Ke, Baiyu Li, Xiangwei Zhu, Huaming Chen, and Guangfu Sun. "NLOS Signal Detection Based on Single Orthogonal Dual-Polarized GNSS Antenna." International Journal of Antennas and Propagation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8548427.
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Nowadays users have a high demand for the accuracy of position and velocity, but errors caused by non-line-of-sight (NLOS) signals cannot be removed effectively. Since the GNSS signal is right-hand circular polarized (RHCP), the axial ratio of the strong NLOS signal is larger than that of the Line-of-Sight (LOS) signal. Based on the difference of the axial ratio, a method for NLOS signal detection using single orthogonal dual-polarized antenna is proposed. The antenna has two channels to receive two orthogonal linear polarized components of the incoming signals. Parallel cross-cancellation is used to remove the LOS signal while maintaining most of the NLOS signals from the receiving signals. The residual NLOS signals are then detected by conventional GNSS digital processor in real time without any prior knowledge of their characteristics. The proposed method makes use of the polarization and spatial information and can detect long delay NLOS signal by miniature and inexpensive receiver GNSS. The effectiveness of the proposed method is confirmed by simulation data.
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Li, Jinwang, Tongyue Gao, Xiaobing Wang, Weiping Guo, and Daizhuang Bai. "Study on the UWB location algorithm in the NLOS environment." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2400/1/012043.
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Abstract At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.
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Cheng, Long, Yong Wang, Mingkun Xue, and Yangyang Bi. "An Indoor Robust Localization Algorithm Based on Data Association Technique." Sensors 20, no. 22 (November 18, 2020): 6598. http://dx.doi.org/10.3390/s20226598.
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As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.
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Lee, Juyul, Myung-Don Kim, Hyun Kyu Chung, and Jinup Kim. "NLOS Path Loss Model for Low-Height Antenna Links in High-Rise Urban Street Grid Environments." International Journal of Antennas and Propagation 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/651438.
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This paper presents a NLOS (non-line-of-sight) path loss model for low-height antenna links in rectangular street grids to account for typical D2D (device-to-device) communication link situations in high-rise urban outdoor environments. From wideband propagation channel measurements collected in Seoul City at 3.7 GHz, we observed distinctive power delay profile behaviors between 1-Turn and 2-Turn NLOS links: the 2-Turn NLOS has a wider delay spread. This can be explained by employing the idea that the 2-Turn NLOS has multiple propagation paths along the various street roads from TX to RX, whereas the 1-Turn NLOS has a single dominant propagation path from TX to RX. Considering this, we develop a path loss model encompassing 1-Turn and 2-Turn NLOS links with separate scattering and diffraction parameters for the first and the second corners, based on the Uniform Geometrical Theory of Diffraction. In addition, we consider the effect of building heights on path loss by incorporating an adjustable “waveguide effect” parameter; that is, higher building alleys provide better propagation environments. When compared with field measurements, the predictions are in agreement.
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Chen, Shiwa, Jianyun Zhang, Yunxiang Mao, Chengcheng Xu, and Yu Gu. "Efficient Distributed Method for NLOS Cooperative Localization in WSNs." Sensors 19, no. 5 (March 7, 2019): 1173. http://dx.doi.org/10.3390/s19051173.
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The accuracy of cooperative localization can be severely degraded in non-line-of-sight (NLOS) environments. Although most existing approaches modify models to alleviate NLOS impact, computational speed does not satisfy practical applications. In this paper, we propose a distributed cooperative localization method for wireless sensor networks (WSNs) in NLOS environments. The convex model in the proposed method is based on projection relaxation. This model was designed for situations where prior information on NLOS connections is unavailable. We developed an efficient decomposed formulation for the convex counterpart, and designed a parallel distributed algorithm based on the alternating direction method of multipliers (ADMM), which significantly improves computational speed. To accelerate the convergence rate of local updates, we approached the subproblems via the proximal algorithm and analyzed its computational complexity. Numerical simulation results demonstrate that our approach is superior in processing speed and accuracy to other methods in NLOS scenarios.
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Ganis, Laura, and Tatiana Christides. "Are We Neglecting Nutrition in UK Medical Training? A Quantitative Analysis of Nutrition-Related Education in Postgraduate Medical Training Curriculums." Nutrients 13, no. 3 (March 16, 2021): 957. http://dx.doi.org/10.3390/nu13030957.
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Suboptimal nutrition is a major cause of morbidity and mortality in the United Kingdom (UK). Although patients cite physicians as trusted information sources on diet and weight loss, studies suggest that the management of nutrition-related disorders is hindered by insufficient medical education and training. Objectives of this study were to: (1) Quantify nutrition-related learning objectives (NLOs) in UK postgraduate medical training curriculums and assess variation across specialties; (2) assess inclusion of nutrition-related modules; (3) assess the extent to which NLOs are knowledge-, skill-, or behaviour-based, and in which Good Medical Practice (GMP) Domain(s) they fall. 43 current postgraduate curriculums, approved by the General Medical Council (GMC) and representing a spectrum of patient-facing training pathways in the UK, were included. NLOs were identified using four keywords: ‘nutrition’, ‘diet’, ‘obesity’, and ‘lifestyle’. Where a keyword was used in a titled section followed by a number of objectives, this was designated as a ‘module’. Where possible, NLOs were coded with the information to address objective 3. A median of 15 NLOs (mean 24) were identified per curriculum. Eleven specialties (25.6%) had five or less NLOs identified, including General Practice. Surgical curriculums had a higher number of NLOs compared with medical (median 30 and 8.5, respectively), as well as a higher inclusion rate of nutrition-related modules (100% of curriculums versus 34.4%, respectively). 52.9% of NLOs were knowledge-based, 34.9% skill-based, and 12.2% behaviour-based. The most common GMP Domain assigned to NLOs was Domain 1: Knowledge, Skills and Performance (53.0%), followed by Domain 2: Safety and Quality (20.6%), 3: Communication, Partnership and Teamwork (18.7%), and 4: Maintaining Trust (7.7%). This study demonstrates considerable variability in the number of nutrition-related learning objectives in UK postgraduate medical training. As insufficient nutrition education and training may underlie inadequate doctor-patient discussions, the results of this analysis suggest a need for further evaluation of nutrition-related competencies in postgraduate training.
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Wu, Cheng, Jianjiang Liu, Xin Huang, Zheng-Ping Li, Chao Yu, Jun-Tian Ye, Jun Zhang, et al. "Non–line-of-sight imaging over 1.43 km." Proceedings of the National Academy of Sciences 118, no. 10 (March 3, 2021): e2024468118. http://dx.doi.org/10.1073/pnas.2024468118.
Full textAbstract:
Non–line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial–temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial–temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.
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Si, Minghao, Yunjia Wang, Shenglei Xu, Meng Sun, and Hongji Cao. "A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification." Applied Sciences 10, no. 3 (February 2, 2020): 956. http://dx.doi.org/10.3390/app10030956.
Full textAbstract:
In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.
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Chen, Xiaojie, Mengyue Li, Tiantian Chen, and Shuyue Zhan. "Long-Range Non-Line-of-Sight Imaging Based on Projected Images from Multiple Light Fields." Photonics 10, no. 1 (December 26, 2022): 25. http://dx.doi.org/10.3390/photonics10010025.
Full textAbstract:
Non-line-of-sight (NLOS) imaging technology has shown potential in several applications, such as intelligent driving, warfare and reconnaissance, medical diagnosis, and disaster rescue. However, most NLOS imaging systems are expensive and have a limited detection range, which hinders their utility in real-world scenarios. To address these limitations, we designed an NLOS imaging system, which is capable of long-range data acquisition. We also introduce an NLOS object imaging method based on deep learning, which makes use of long-range projected images from different light fields to reconstruct hidden objects. The method learns the mapping relationships of projected images and objects and corrects the image structure to suppress the generation of artifacts in order to improve the reconstruction quality. The results show that the proposed method produces fewer artifacts in reconstructions, which are close to human subjective perception. Furthermore, NLOS targets can be reconstructed even if the distance between the detection device and the intermediate surface exceeds 50 m.
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Bian, Zhentian, Long Cheng, and Yan Wang. "A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment." Journal of Sensors 2021 (November 13, 2021): 1–15. http://dx.doi.org/10.1155/2021/6125890.
Full textAbstract:
While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.
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Hua, Jingyu, Yejia Yin, Weidang Lu, Yu Zhang, and Feng Li. "NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks." Sensors 18, no. 9 (September 7, 2018): 2991. http://dx.doi.org/10.3390/s18092991.
Full textAbstract:
The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.
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Sun, Rui, Li-Ta Hsu, Dabin Xue, Guohao Zhang, and Washington Yotto Ochieng. "GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System." Journal of Navigation 72, no. 3 (December 6, 2018): 685–701. http://dx.doi.org/10.1017/s0373463318000899.
Full textAbstract:
The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.
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Yao, Hexiong, Zhiqiang Dai, Weixiang Chen, Ting Xie, and Xiangwei Zhu. "GNSS Urban Positioning with Vision-Aided NLOS Identification." Remote Sensing 14, no. 21 (October 31, 2022): 5493. http://dx.doi.org/10.3390/rs14215493.
Full textAbstract:
The global navigation satellite system (GNSS) has played an important role in a broad range of consumer and industrial applications. In particular, cities have become GNSS major application scenarios; however, GNSS signals suffer from blocking, reflection and attenuation in harsh urban environments, resulting in diverse received signals, e.g., non-line-of-sight (NLOS) and multipath signals. NLOS signals often cause severe deterioration in positioning, navigation, and timing (PNT) solutions, which should be identified and excluded. In this paper, we propose a vision-aided NLOS identification method to augment GNSS urban positioning. A skyward omnidirectional camera is installed on a GNSS antenna to collect omnidirectional images of the sky region. After being rectified, these images are processed for sky region segmentation, which is improved by leveraging gradient information and energy function optimization. Image morphology processing is further employed to smooth slender boundaries. After sky region segmentation, the satellites are projected onto the omnidirectional image, from which NLOS satellites are identified. Finally, the identified NLOS satellites are excluded from GNSS PNT estimation, promoting accuracy and stability. Practical test results show that the proposed sky region segmentation module achieves over 96% accuracy, and that completely accurate NLOS identification is achieved for the experimental images. We validate the performance of our method on public datasets. Compared with the raw measurements without screening, the vision-aided NLOS identification method enables improvements of 60.3%, 12.4% and 63.3% in the E, N, and U directions, respectively, as well as an improvement of 58.5% in 3D accuracy.
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Jin, JiaWei, Long Cheng, and JiaBao Zhou. "A Novel NLOS Suppression Algorithm for Indoor Location based on FCM and REKF." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012067. http://dx.doi.org/10.1088/1742-6596/2216/1/012067.
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Abstract Abstarct. The indoor positioning based on wireless sensor networks (WSN) has become one of the research hotpots. However, the NLOS propagation of the distance signals greatly challenges the accuracy and robustness of the algorithm. In this paper, we take the suppression of NLOS as the core goal and proposed the FCM-REKF-based positioning method. We firstly identify the signal states through the fuzzy c-means clustering (FCM), for the measurement distance judged to be NLOS, a refactoring method based on FCM is used. Then the corrected distance is smoothed by Kalman filter, and the Robust Extended Kalman Filter is used to calculate the final position. The simulation results show that our method has higher accuracy than EKF, REKF and IMM-EKF under NLOS environment.
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Zou, Yanbin, and Huaping Liu. "An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization." Sensors 20, no. 5 (March 4, 2020): 1403. http://dx.doi.org/10.3390/s20051403.
Full textAbstract:
In time-of-arrival (TOA) localization systems, errors caused by non-line-of-sight (NLOS) signal propagation could significantly degrade the location accuracy. Existing works on NLOS error mitigation commonly assume that NLOS error statistics or the TOA measurement noise variances are known. Such information is generally unavailable in practice. The goal of this paper is to develop an NLOS error mitigation scheme without requiring such information. The core of the proposed algorithm is a constrained least-squares optimization, which is converted into a semidefinite programming (SDP) problem that can be easily solved by using the CVX toolbox. This scheme is then extended for cooperative source localization. Additionally, its performance is better than existing schemes for most of the scenarios, which will be validated via extensive simulation.
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Su, Szu Lin, Yi Wen Su, Ho Nien Shou, and Chien Sheng Chen. "Non-Line-of-Sight Error Mitigation in Wireless Communication Systems." Advanced Engineering Forum 1 (September 2011): 173–77. http://dx.doi.org/10.4028/www.scientific.net/aef.1.173.
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When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).
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Dong, Jia Zhi, Yu Wen Wang, Feng Wei, and Jiang Yu. "A Localization Algorithm Research to Eliminate the Indoor NLOS Error." Advanced Materials Research 989-994 (July 2014): 2232–36. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2232.
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Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.
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Zhang, Li, Hao Zhang, Xue Rong Cui, and T. Aaron Gulliver. "Ultra Wideband Indoor Positioning Using Kalman Filters." Advanced Materials Research 433-440 (January 2012): 4207–13. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4207.
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A time-difference-of-arrival (TDOA) positioning technique for indoor ultra wideband (UWB) systems is presented. Non-line-of-sight (NLOS) propagation error is a major source of error in positioning systems. Therefore an NLOS mitigation technique employing a Kalman filter is utilized to reduce the NLOS errors in indoor UWB environments. An extended Kalman filter (EKF) is used to process the TDOA data for mobile positioning and tracking. Performance results are presented which show that the proposed scheme can significantly improve the positioning accuracy in a UWB environment.
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Xie, Li Ying, and Jin Xin Ruan. "A TOA Location Method Using ROS Scattering Model in NLOS Environment." Applied Mechanics and Materials 321-324 (June 2013): 858–61. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.858.
Full textAbstract:
In most cellular propagation environments, a LOS propagation path does not exist to all of the BSs that participate in locating a target MS. The signal received by the receiver is a multipath and interference mixed signal, which is NLOS signal. In NLOS conditions, the traditional location algorithms perform poorly. Therefore, a scattering-model-based location method for cellular network was proposed in this paper. Based on the Ring of Scatters (ROS) model, with the use of the mean value of the TOA measurements, the method mitigated the effects of NLOS.
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Wu, Shixun, Shengjun Zhang, Kai Xu, and Darong Huang. "Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network." Wireless Communications and Mobile Computing 2019 (March 28, 2019): 1–8. http://dx.doi.org/10.1155/2019/2707469.
Full textAbstract:
In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.
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Sun, Chao, Meiting Xue, Nailiang Zhao, Yan Zeng, Junfeng Yuan, and Jilin Zhang. "A Deep Learning Method for NLOS Error Mitigation in Coastal Scenes." Journal of Marine Science and Engineering 10, no. 12 (December 8, 2022): 1952. http://dx.doi.org/10.3390/jmse10121952.
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With the widespread use of automatic identification systems (AISs), some ships use deceptive information or intentionally close their AISs to conceal their illegal activities or evade the supervision of maritime departments. Although radar measurements can be effectively utilized to evaluate the credibility of received AIS data, the propagation of non-line-of-sight (NLOS) signal conditions is an important factor that affects location accuracy. This study addresses the NLOS problem in a special geometric dilution of precision (GDOP) scenario on a coast and several base stations. We employed data augmentation and a deep residual shrinkage network in order to alleviate the adverse effects of NLOS errors. The results of our simulations demonstrate that the proposed method outperforms other range-based localization algorithms in a mixed LOS/NLOS environment. For a special GDOP scenario with four radars, our algorithm’s root-mean-square error (RMSE) was lower than 180 m.
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Han, Ke, Lingjie Shi, Zhongliang Deng, Xiao Fu, and Yun Liu. "Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability." Sensors 20, no. 4 (February 22, 2020): 1211. http://dx.doi.org/10.3390/s20041211.
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With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively.
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Takayama, Yoji, Takateru Urakubo, and Hisashi Tamaki. "Novel Process Noise Model for GNSS Kalman Filter Based on Sensitivity Analysis of Covariance with Poor Satellite Geometry." Sensors 21, no. 18 (September 9, 2021): 6056. http://dx.doi.org/10.3390/s21186056.
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One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.
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Phaiboon, Supachai, and Pisit Phokharatkul. "Accurate Empirical Path Loss Models with Route Classification for mmWave Communications." International Journal of Antennas and Propagation 2022 (October 20, 2022): 1–9. http://dx.doi.org/10.1155/2022/2780029.
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This paper presents accurate empirical path loss models with route classification for the high band frequency of 5 G wireless. Propagation path routes are mainly classified into line of sight (LOS) and non-line-of-sight (NLOS). The NLOS routes are classified into 2 separate routes, namely, Hard_NLOS and Soft_NLOS. Their path loss models include free-space loss (Lfs) and multiscreen diffraction loss (Lmsd) together with the reflection from the building blocks. However, these NLOS routes can be combined into a single formula. The path loss models were fitted with measured path loss data at frequencies of 28 GHz and 73 GHz. These models are compared with four 5G empirical models, namely 5GCM, 3GPP, METIS, and mmMAGIC. The results show that the separated route models provide good agreement, especially for the hard routes compared with those models and provide the minimum MAE of 4.45 dB, 4.34 dB, and 6.72 dB for the hard route, soft route, and an all-NLOS route, respectively, for the dual-band frequency.
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Bassma, Guermah, Sadiki Tayeb, and El Ghazi Hassan. "GNSS Positioning Enhancement Based on NLOS Multipath Biases Estimation Using Gaussian Mixture Noise." International Journal of Mobile Computing and Multimedia Communications 9, no. 1 (January 2018): 21–39. http://dx.doi.org/10.4018/ijmcmc.2018010102.
Full textAbstract:
Global navigation satellite systems (GNSS) have been widely used in many applications where positioning plays an important role. However, the performances of these applications can be degraded in urban canyons, due to Non-Line-Of-Sight (NLOS) and Multipath interference affecting GNSS signals. In order to ensure high accuracy positioning, this article proposes to model the NLOS and Multipath biases by Gaussian Mixture noise using Expectation Maximization (EM) algorithm. In this context, an approach to estimate the Multipath and NLOS biases for real time positioning is presented and statistical tests for searching the probability distribution of NLOS and Multipath biases are illustrated. Furthermore, a hybrid approach based on PF (Particle Filter) and EM algorithm for estimating user position in hard environment is presented. Using real GPS (Global Positioning System) signal, the efficiency of the proposed approach is shown, and a significant improvement of the positioning accuracy over the simple PF estimation is obtained.
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Gao, Zefu, Yiwen Jiao, Wenge Yang, Xuejian Li, and Yuxin Wang. "A Method for UWB Localization Based on CNN-SVM and Hybrid Locating Algorithm." Information 14, no. 1 (January 12, 2023): 46. http://dx.doi.org/10.3390/info14010046.
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In this paper, aiming at the severe problems of UWB positioning in NLOS-interference circumstances, a complete method is proposed for NLOS/LOS classification, NLOS identification and mitigation, and a final accurate UWB coordinate solution through the integration of two machine learning algorithms and a hybrid localization algorithm, which is called the C-T-CNN-SVM algorithm. This algorithm consists of three basic processes: an LOS/NLOS signal classification method based on SVM, an NLOS signal recognition and error elimination method based on CNN, and an accurate coordinate solution based on the hybrid weighting of the Chan–Taylor method. Finally, the validity and accuracy of the C-T-CNN-SVM algorithm are proved through a comparison with traditional and state-of-the-art methods. (i) Focusing on four main prediction errors (range measurements, maxNoise, stdNoise and rangeError), the standard deviation decreases from 13.65 cm to 4.35 cm, while the mean error decreases from 3.65 cm to 0.27 cm, and the errors are practically distributed normally, demonstrating that after training a SVM for LOS/NLOS signal classification and a CNN for NLOS recognition and mitigation, the accuracy of UWB range measurements may be greatly increased. (ii) After target positioning, the proposed method can realize a one-dimensional X-axis and Y-axis accuracy within 175 mm, and a Z-axis accuracy within 200 mm; a 2D (X,Y) accuracy within 200 mm; and a 3D accuracy within 200 mm, most of which fall within (100 mm, 100 mm, 100 mm). (iii) Compared with the traditional algorithms, the proposed C-T-CNN-SVM algorithm performs better in location accuracy, cumulative error probability (CDF), and root-mean-square difference (RMSE): the 1D, 2D, and 3D accuracy of the proposed method is 2.5 times that of the traditional methods. When the location error is less than 10 cm, the CDF of the proposed algorithm only reaches a value of 0.17; when the positioning error reaches 30 cm, only the CDF of the proposed algorithm remains in an acceptable range. The RMSE of the proposed algorithm remains ideal when the distance error is greater than 30 cm. The results of this paper and the idea of a combination of machine learning methods with the classical locating algorithms for improved UWB positioning under NLOS interference could meet the growing need for wireless indoor locating and communication, which indicates the possibility for the practical deployment of such a method in the future.
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Kubo, Nobuaki, Kaito Kobayashi, and Rei Furukawa. "GNSS Multipath Detection Using Continuous Time-Series C/N0." Sensors 20, no. 14 (July 21, 2020): 4059. http://dx.doi.org/10.3390/s20144059.
Full textAbstract:
The reduction of multipath errors is a significant challenge in the Global Navigation Satellite System (GNSS), especially when receiving non-line-of-sight (NLOS) signals. However, selecting line-of-sight (LOS) satellites correctly is still a difficult task in dense urban areas, even with the latest GNSS receivers. This study demonstrates a new method of utilization of C/N0 of the GNSS to detect NLOS signals. The elevation-dependent threshold of the C/N0 setting may be effective in mitigating multipath errors. However, the C/N0 fluctuation affected by NLOS signals is quite large. If the C/N0 is over the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily. To overcome this issue, we focused on the value of continuous time-series C/N0 for a certain period. If the C/N0 of the satellite was less than the determined threshold, the satellite was not used for positioning for a certain period, even if the C/N0 recovered over the threshold. Three static tests were conducted at challenging locations near high-rise buildings in Tokyo. The results proved that our method could substantially mitigate multipath errors in differential GNSS by appropriately removing the NLOS signals. Therefore, the performance of real-time kinematic GNSS was significantly improved.
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Kato, Shodai, Mitsunori Kitamura, Taro Suzuki, and Yoshiharu Amano. "NLOS Satellite Detection Using a Fish-Eye Camera for Improving GNSS Positioning Accuracy in Urban Area." Journal of Robotics and Mechatronics 28, no. 1 (February 18, 2016): 31–39. http://dx.doi.org/10.20965/jrm.2016.p0031.
Full textAbstract:
[abstFig src='/00280001/03.jpg' width=""300"" text='NLOS satellites detection method' ]In recent years, global navigation satellite systems (GNSSs) have been widely used in intelligent transport systems (ITSs), and many countries have been rapidly improving the infrastructure of their satellite positioning systems. However, there is a serious problem involving the use of kinematic GNSS positioning in urban environments, which stems from significant positioning errors introduced by non-line-of-sight (NLOS) satellites blocked by obstacles. Therefore, we propose the method for positioning based on NLOS satellites detection using a fish-eye camera. In general, it is difficult to robustly extract an obstacle region from the fish-eye image because the image is affected by cloud cover, illumination conditions, and weather conditions. We extract the obstacle region from the image by tracking image feature points in sequential images. Because the obstacle region on the image moves larger than the sky region, the obstacle region can be determined by performing image segmentation and by using feature point tracking techniques. Finally, NLOS satellites can be identified using the obstacle region on the image. The evaluation results confirm the GNSS positioning accuracy without the NLOS satellites was improved compared with using all observed satellites, and confirm the effectiveness of the proposed technique and the feasibility of implementing its highly accurate positioning capabilities in urban environments.