Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Xiong, Wenxin; Schindelhauer, Christian; So, Hing Cheung; Wang, Zhi

doi:10.1007/s00034-021-01800-y

Cited by 28 publications

(11 citation statements)

References 29 publications

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“…Indoor Positioning Method. According to the different ranging methods, common RFID indoor positioning methods are divided into four types: RSSI [15], TOA [16], TDOA [17], and AOA [18].…”

Section: Related Researchmentioning

confidence: 99%

Fixed Assets Positioning Management Based on Nonlinear Difference Algorithm

2022

Mobile Information Systems

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Fixed assets are an enterprise’s core means of production and a vital asset for the enterprise’s sustainability. Good fixed asset management not only helps to enhance asset utilization but also helps to prevent idleness and asset loss, both of which are vital in the success of the enterprise. To improve fixed asset management, it needs not only a good asset management system but also advanced asset monitoring and management technology. As a result, to address the issues of difficult positioning, tracking, and low management efficiency in enterprise’s asset management, this paper optimizes the commonly used the LANDMARC positioning algorithm and implements an improved algorithm based on Lagrange nonlinear interpolation. It is also used in fixed asset positioning management to overcome the problem of increasing the density of auxiliary tags to enhance positioning accuracy, which leads to higher costs and greater radio frequency interference. Simultaneously, a set of RFID-based fixed asset management systems was built and implemented, starting with the application requirements of enterprises and combining the optimization algorithm with FRID technology. Finally, a simulation experiment on the application of the Lagrange nonlinear interpolation algorithm in fixed asset positioning management is carried out and a comparison with other algorithms is performed. According to the simulation findings, the new algorithm has enhanced the positioning accuracy. The system can perform all-around dynamic tracking and positioning management of diverse assets, optimize the use efficiency and management level of assets, and improve the intelligent level of enterprise asset management.

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“…Indoor Positioning Method. According to the different ranging methods, common RFID indoor positioning methods are divided into four types: RSSI [15], TOA [16], TDOA [17], and AOA [18].…”

Section: Related Researchmentioning

confidence: 99%

Fixed Assets Positioning Management Based on Nonlinear Difference Algorithm

2022

Mobile Information Systems

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“…HQ technique in [27], and the MP-assisted IRLS in [28] are three other representative TOA positioning schemes from the literature that adopt the strategy of statistical robustification as well. They lead to O(N LPNN L), O(N HQ KL), and O(N IRLS L) complexity, respectively, where N LPNN is the number of iterations in the numerical implementation of LPNN, N HQ the number of steps needed for the HQ algorithm to converge, and N IRLS that of the IRLS iterations.…”

Section: Complexity and Convergence Propertiesmentioning

confidence: 99%

“…to sensor network node localization rather than single-source positioning. In [27], the intractable range-based Welsch loss minimization problem:…”

Section: Introductionmentioning

confidence: 99%

“…While implementing the convex programs via interior-point methods is known to scale badly with L, numerical realization of the neurodynamic approach in [26] usually takes several thousands of iterations to reach equilibrium [50]. Despite the low-complexity advantage of HQ, the squared-range formulation (11) in [27] inherently suffers from an impaired statistical efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Robust Elliptic Localization Using Worst-Case Formulation and Convex Approximation

Xiong

Schindelhauer

et al. 2019

2019 16th Workshop on Positioning, Navigation and Communications (WPNC)

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This article considers the problem of source localization (SL) using possibly unreliable timeof-arrival (TOA) based range measurements. Adopting the strategy of statistical robustification, we formulate the TOA SL as minimization of a versatile loss that possesses resistance against the occurrence of outliers. We then present an alternating direction method of multipliers (ADMM) to tackle the nonconvex optimization problem in a computationally attractive iterative manner. Moreover, we prove that the solution obtained by the proposed ADMM will correspond to a Karush-Kuhn-Tucker point of the formulation when the algorithm converges, and discuss reasonable assumptions about the robust loss function under which the approach can be theoretically guaranteed to be convergent. Numerical investigations demonstrate the superiority of our method over many existing TOA SL schemes in terms of positioning accuracy and computational simplicity. In comparison with its competitors, the proposed ADMM is in particular observed to produce location estimates with mean squared error performance closer to the Cramér-Rao lower bound in our simulations of impulsive noise environments.

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“…There are two types of localization algorithms commonly used in WSNs: range-based and range-free [6]. Rangebased node localization algorithms include angle of arrival (AOA) [7], time of arrival (TOA) [8], time difference of arrival (TDOA) [9], and methods based on received signal strength indication (RSSI) [10], etc. However, the range-based node localization algorithms require high hardware requirements and are susceptible to interference from external environmental factors in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

2022

KSII TIIS

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The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

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Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Cited by 28 publications

References 29 publications

Fixed Assets Positioning Management Based on Nonlinear Difference Algorithm

Fixed Assets Positioning Management Based on Nonlinear Difference Algorithm

Robust Elliptic Localization Using Worst-Case Formulation and Convex Approximation

Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

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