Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach

Meghani, Sunil Kumar; Asif, Muhammad; Awin, Faroq; Tepe, Kemal

doi:10.1109/access.2019.2904201

“…The effects of MPCs can be modeled as a bias error parameter in the model of the localization problem, and can be solved by using several tools such as convex optimization and sparse estimation methods [150]. It was demonstrated that fuzzy logic can be a useful tool for inferring the MPCs error with considerably less computational demand [149]. Furthermore, when the sensor nodes change their positions in time, several techniques based on the extended Kalman filter and particle filter can be used to track the positions of the TN and bias error caused by the MPCs [148,151,171].…”

Section: ) Mitigation Of Multipath Componentsmentioning

confidence: 99%

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Najarro¹,

Song²,

Kim³

2021

Preprint

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With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

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“…Model level [147][148][149][150][151][152] The effects of the MPCs are modeled as additive biases. The sparsity of the MPCS on the localization model can be exploited.…”

Section: Mitigationmentioning

confidence: 99%

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Najarro¹,

Song²,

Kim³

2021

Preprint

View full text Add to dashboard Cite

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

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“…However, the overfitting & underfitting, parameter tuning and processing time of data from CNN models give challenges to the UWB localization. Most recent works on deep learning for UWB localization are explained in References [23][24][25]. The discussed localization systems from References [23][24][25] effectively minimize the ranging errors and computational burden and achieve excellent localization performance with much lower network complexity for UWB indoor localization.…”

Section: Related Workmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

Poulose

¹

,

Han

²

2020

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Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

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Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach

Cited by 20 publications

References 27 publications

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

UWB Indoor Localization Using Deep Learning LSTM Networks

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