Dynamic Multidimensional Scaling Algorithm for 3-D Mobile Localization

Cui, Wei; Wu, Chengdong; Meng, Wei; Li, Bing; Zhang, Yunzhou; Xie, Lihua

doi:10.1109/tim.2016.2608518

Cited by 35 publications

(28 citation statements)

References 42 publications

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“…For the comparison with existing methods (UKF, EKF, nonlinear regression (NR), MDS, RVFL) [11], [17], [22], [23], we implemented their algorithms in the same environment of our lab with the best choices of parameters to ensure the fairness of the comparison. The results of moving horizon estimation and particle filter method are from [32], [36], [41].…”

Section: Comparison With Existing Range-based Localization Methodsmentioning

confidence: 99%

“…However, there are limitations of existing range-based localization algorithms. First, many algorithms [11]- [13] such as multilateration and multidimensional scaling (MDS) algorithms leveraging on optimization require that the mobile Xu robot receive multiple concurrent range measurements. They may have relatively low localization accuracy when the range sensors cannot support multi-channels.…”

Section: Introductionmentioning

confidence: 99%

“…From the experimental results in the existing range-based localization methods [11], [17], [22], [23], we find that their performance in the altitude direction is generally not as good as other directions. Some possible solutions include: (i) Adding altitude sensors such as Laser beam or Lidar to measure the altitude, but it requires the ground to be even; (ii) Placing anchors on the ceiling, but it may also be difficult for many environments.…”

Section: Introductionmentioning

confidence: 99%

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Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals. Special emphasis will be on range-based localization. Range and trajectory smoothness constraints are constructed in a position graph, then the robot trajectory over a sliding window is estimated by a graph based optimization algorithm. Moreover, convergence analysis of the algorithm is provided, and the effects of the number of iterations and window size in the optimization on the localization accuracy are analyzed. Extensive experiments on quadcopter under a variety of scenarios verify the effectiveness of the proposed algorithm and demonstrate a much higher localization accuracy than the existing range-based localization methods, especially in the altitude direction.Index Terms-Graph optimization approach, Range-based localization, 2-D and 3-D spaces, Ultra-wide band radio.

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Section: Comparison With Existing Range-based Localization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…Then, the residual weighted method is used to obtain the final positioning results. Base on the range distance in each sampling period, Cui et al [12] use multidimensional scaling localization algorithm to evaluate the location of the target and fit the result of the estimation by polynomial. The estimation results of current position can be corrected effectively, and the method is proven to achieve high positioning accuracy in indoor environment.…”

Section: Related Workmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

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The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node's localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithmprovidesabetterpositionestimationwithlesslocalizationerror.

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“…Location is a significant application of WSN. The WSN-based location methods have widely been used for indoor location [2,3].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2017

Journal of Sensors

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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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Dynamic Multidimensional Scaling Algorithm for 3-D Mobile Localization

Cited by 35 publications

References 42 publications

Graph Optimization Approach to Range-Based Localization

Graph Optimization Approach to Range-Based Localization

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

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

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