An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization

Xu, Chentao; Liu, Qingshan

doi:10.1016/j.neunet.2021.11.012

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…It is equivalent to the least squares method when the clock asynchronization is zero. Similar work has been explored by other scholars [32]. Furthermore, a residual based weighted least square (RWLS) algorithm is proposed to locate the mobile station, which can achieve a high precision by reducing the measurement's weight with big residual [33].…”

Section: Introductionmentioning

confidence: 80%

An Attack-Resistant Localization Algorithm with Adaptive Weights for Range-Based Wireless Sensor Networks

Wang¹,

Liu²,

Xu³

2022

Preprint

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Localization is a crucial problem for robot control in the wireless sensors network with malicious nodes. In this paper, an attack-resistant localization algorithm with adaptive weight is proposed to achieve an accurate localization without any prior information of malicious anchors. The core idea of the proposed algorithm is adjusting the weights of malicious anchors based on the estimation for the attack value of malicious anchors to eliminate the influence of malicious anchors. Meanwhile, the subfunction's gradient is used to avoid the local minimum of non-convex function based on the particularity of the positioning problem. Three kinds of simulations are conducted to illustrate that the proposed algorithm can achieve an accurate localization in different types of attacks. Furthermore, two comparisons with other two localization methods are presented to show the advantage of the proposed algorithm by simulations and experiments.

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Section: Introductionmentioning

confidence: 80%

An Attack-Resistant Localization Algorithm with Adaptive Weights for Range-Based Wireless Sensor Networks

Wang¹,

Liu²,

Xu³

2022

Preprint

Self Cite

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show abstract

“…As mentioned above, various techniques are used for localization. The performance of localization is hindered by various factors in the application of these techniques [11][12][13][14]. However, in this paper, we will introduce them in three main categories.…”

Section: The Factors For Degrading Performance Of Localizationmentioning

confidence: 99%

When Wireless Localization Meets Artificial Intelligence: Basics, Challenges, Synergies, and Prospects

Cha,

Lee,

Ozger

et al. 2023

Applied Sciences

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The rapid development of information communication and artificial intelligence (AI) technology is driving innovation in various new application fields such as autonomous driving, augmented reality, and the metaverse. In particular, the advancement of wireless localization technology plays a great role in these cutting-edge technologies. However, traditional wireless localization systems rely on the global navigation satellite system (GNSS), which is ineffective in indoor or underground environments. To overcome this issue, indoor positioning systems (IPS) have gained attention, and various localization techniques utilizing wireless communication were studied. Subsequently, AI technologies are improving the performance of wireless localization and addressing problems that were previously difficult to solve. In this paper, we summarize wireless localization techniques and define the factors that impede their performance. Furthermore, we categorize AI algorithms and present examples of how they can be used to address these hindering factors. Finally, we propose open research directions and prospects for AI-assisted wireless localization.

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“…In recent years, neural networks have been extensively studied to solve real world problems. In previous studies, ordinary differential equations model were used to model neural networks, 1‐3 while partial differential equations have unique advantages in simulating neuronal dynamics. Due to the reaction‐diffusion term, the reaction‐diffusion neural networks (RDNNs) has complicated spatial dynamical behaviors 4‐7 .…”

Section: Introductionmentioning

confidence: 99%

Synchronization of reaction‐diffusion neural networks with distributed delay via quantized boundary control

Zhang

Han

Zhang

et al. 2023

Adaptive Control & Signal

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This paper investigates the synchronization of reaction-diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers respectively subject to constant and adaptive coefficients are carried out. Worth mentioning that the adaptive feedback gain is a matrix in this paper rather than a one-dimensional variable in most of the existing literatures. Using the Lyapunov functional, the sufficient conditions for delay-dependent synchronization are obtained through linear matrix inequalities. The effectiveness of the proposed control strategy is illustrated via two examples.

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An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization

Cited by 17 publications

References 37 publications

An Attack-Resistant Localization Algorithm with Adaptive Weights for Range-Based Wireless Sensor Networks

An Attack-Resistant Localization Algorithm with Adaptive Weights for Range-Based Wireless Sensor Networks

When Wireless Localization Meets Artificial Intelligence: Basics, Challenges, Synergies, and Prospects

Synchronization of reaction‐diffusion neural networks with distributed delay via quantized boundary control

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