An improved NLOS detection scheme using stochastic characteristics for indoor localization

Horiba, Manato; Okamoto, Eiji; Shinohara, Toshiko; Matsumura, Katsuhiko

doi:10.1109/icoin.2015.7057951

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…There are so many research prototypes focused on this. [3][4][5][6] The main directions of research aim to resolve multipath and penetrate obstacles. [4][5][6][7] On the other hand, some mature commercial localizations have emerged, such as PLUS and Ubisense.…”

Section: Related Workmentioning

confidence: 99%

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A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments

Tong

Deng

Zhang

et al. 2018

International Journal of Distributed Sensor Networks

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Indoor localization systems using received signal strength indicator are very popular for their low power and low complexity, but some drawbacks limit their accuracy, especially in harsh environments, such as multipath and fluctuation. Most existing approaches solve the problem by “fingerprinting.” However, “fingerprinting” based algorithms are unsuitable for changeable environments like construction, since they all demand prior knowledge of the environment. This article studies a novel localization system to achieve an acceptable accuracy position using received signal strength indicator for harsh environments like construction. Based on analysis of the targets’ behavior pattern, we first use curve fitting to filter the distance derived from received signal strength indicator. And then, we propose a distance ratio location algorithm to estimate the targets’ positions. Furthermore, Kalman filter is considered to smooth the position results. This method has been applied in the “Monitoring and Control System for Underground Tunneling Based on Cyber Physical System” Project in Wuhan for tracking workers and vehicles. Practice results show that our system has an acceptable accuracy.

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Section: Related Workmentioning

confidence: 99%

“…[3][4][5][6] The main directions of research aim to resolve multipath and penetrate obstacles. [4][5][6][7] On the other hand, some mature commercial localizations have emerged, such as PLUS and Ubisense. They are both based on UWB and have a very high precision (approximately 10-15 cm).…”

Section: Related Workmentioning

confidence: 99%

A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments

Tong

Deng

Zhang

et al. 2018

International Journal of Distributed Sensor Networks

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“…How to detect and identify NLOS has been paid close attention by scholars in this field, which is to mitigate the error caused by multipath effect, such as Perz Cruz has proposed that the error caused by NLOS propagation can be viewed as a random variable, and derived its probability density function (PDF) [13]. Horiba has detected the condition of NLOS by the random characteristics of the measurement error and the modified iterative minimum residual method (IMR) [14]. Liu F. has proposed a method that use complementary Kalman filters to integrate UWB and IMU data to improve positioning accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

Yang

Wang

et al. 2019

Electronics

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In the global positioning system (GPS) denied environment, an indoor positioning system based on ultra-wide band (UWB) technology has been utilized for target location and navigation. It can provide a more accurate positioning measurement than those based on received signal strength (RSS). Although promising, it suffers from some shortcomings that base stations should be preinstalled to obtain reference coordinate information, just as navigation satellites in the GPS system. In order to improve the positioning accuracy, a large number of base stations should be preinstalled and assigned coordinates in the large-scale network. However, the coordinate setup process of the base stations is cumbersome, time consuming, and laborious. For a class of linear network topology, a semi-autonomous coordinate configuration technology of base stations is designed, which refers to three conceptions of segmentation, virtual triangle, and bidirectional calculation. It consists of two stages in every segment: Forward and backward. In the forward stage, it utilizes the manual coordinate setup method to deal with the foremost two base stations, and then the remaining base stations autonomously calculate their coordinates by building the virtual triangle train. In the backward stage, the reverse operation is performed, but the foremost two base stations of the next segment should be used as the head. In the last segment, the last two base stations should be used as the head. Integrating forward and backward data, the base stations could improve their location accuracy. It is shown that our algorithm is feasible and practical in simulation results and can dramatically reduce the system configuration time. In addition, the error and maximum base station number for one segment caused by our algorithm are discussed theoretically.

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“…In the localization algorithms, many algorithms are studied to mitigate the interference of NLOS factors [10][11][12][13][14][15][16][17]. Chen [10] proposed a residual weighting (RWGH) algorithm, which can mitigate NLOS errors to a certain extent, but the computational complexity is high.…”

Section: Introductionmentioning

confidence: 99%

“…Li [15] proposed an iterative minimum residual algorithm, which iteratively selects the minimum residual combination as the final estimated position of the mobile station (MS) by iterating the residual size in each combination whose value is less than the predetermined threshold. Horiba and coworkers [16,17] used a TOA/AOA hybrid positioning method to improve the performance of the iterative minimum residual algorithm by selecting the appropriate iterative minimum residual criterion. In References [18][19][20], the NLOS mitigation algorithms proposed by the authors can reduce the NLOS errors without prior knowledge of the NLOS errors.…”

Section: Introductionmentioning

confidence: 99%

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

et al. 2019

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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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An improved NLOS detection scheme using stochastic characteristics for indoor localization

Cited by 10 publications

References 10 publications

A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments

A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments

Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

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