Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Yu, Xiaoqing; Ji, Peng; Wang, Ying; Chen, Hao

doi:10.1155/2017/5325174

Cited by 12 publications

(8 citation statements)

References 25 publications

(27 reference statements)

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“…NLOS identification is profoundly significant in most range-based algorithms. Hence, some methods have been proposed to address this issue better [ 9 , 10 , 11 , 12 , 13 ]. Hua proposed an NLOS identification and positioning algorithm based on a localization residual in WSN in [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…The main idea of this paper is the identification of NLOS nodes based on the statistical model of the measurement and the NLOS errors where they utilized an NP method to judge a threshold. Yu put forward a mean shift-based mobile localization method in mixed LOS/NLOS environments for a wireless sensor network in [ 13 ]. A novel mobile localization method is presented in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter, but the NLOS identification is based on the hypotheses and alternatives methods.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, in range-based indoor localization, the mitigation of NLOS errors is profoundly critical to improve the indoor localization accuracy. Many strategies have been proposed in order to mitigate NLOS effects [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks

Cheng

Wang

et al. 2019

Sensors

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks

Cheng

Wang

et al. 2019

Sensors

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“…In [ 33 , 34 , 35 , 36 , 37 , 38 , 39 ], the NLOS identification algorithms have been proposed to realize mobile localization in NLOS environments. In [ 33 , 34 , 35 ], the NLOS measurements are identified and discarded in location estimation.…”

Section: Related Workmentioning

confidence: 99%

“…The key technology of the proposed method is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. In [ 39 ], the data association method is proposed to mitigate the NLOS measurements. In this method, the binary hypothesis is carried out to detect the measurements that contain the NLOS errors.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Wang

Hang

Cheng

et al. 2018

Sensors

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In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

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Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Bhat

Santhosh

2020

Wireless Pers Commun

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Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

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Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Cited by 12 publications

References 25 publications

A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks

A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks

A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

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