3D WLS hybrid and non hybrid localization using TOA, TDOA, azimuth and elevation

Halima, Nadhir Ben; Boujemâa, Hatem

doi:10.1007/s11235-018-0468-7

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…Estimation in a 3-dimensional environment can produce a larger error compared to a 2dimensional and involves more anchors. The location estimation optimization methods proposed by researchers include residual weighting [90], selection of subset anchors [53] [54] [148], weighted least squares, and combining techniques ranging [149] and least square matrix [123]. Ben et.al.…”

Section: The Nlos and Multipath Environmentmentioning

confidence: 99%

Future Research and Trends in Ultra-Wideband Indoor Tag Localization

Hapsari,

Munadi,

Erfianto

et al. 2024

IEEE Access

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Indoor tag localization technology is one area of research applied to positioning, tracking, and navigation. UWB is a signal that is widely used for indoor tag localization with the consideration of having higher accuracy, wide bandwidth, and low power consumption. The diversity and breadth of research in the field of indoor tag localization can be confusing for new researchers to find out how this research has progressed until this day. The use of other sensors besides UWB in indoor tag localization is an alternative solution offered, thus obscuring the trend of UWB research on indoor tag localization. The way to find out whether indoor tag localization research using UWB still has a good trend and has research opportunities is to read paper reviews from previous researchers. However, until now no one has reviewed the previous paper review using the SMS and SLR methods. The SMS method is quantitative which directs the paper collection to the intended research area so it can be mapped properly. SMS is the process of identifying, categorizing, and analyzing existing literature that is relevant to a certain research topic. Meanwhile, the SLR method is qualitative which makes paper discussions sharper and more focused. SLR is the process of collecting and critically analyzing multiple research studies or papers through a systematic process. It aims to synthesize the findings of the studies and conclude the state of the art on the topic. This paper describes the literacy results of the paper using the SMS and SLR methods to thoroughly discuss research developments on UWB indoor tag localization. The discussion includes research developments and topic areas of indoor tag localization from 2017-2022, trends in the use of UWB in indoor tag localization research, issues and metrics that are carried out in indoor tag localization research, and opportunities for future research. Literacy results show that machine learning, filtering, and sensor fusion are topic areas that are currently being researched on UWB indoor tag localization. In general, every research optimizes performance metrics, namely accuracy, scalability, energy efficiency, latency, cost, and complexity. Each optimized metric related to different issues will be represented in the form of a taxonomy for easy understanding and explained in detail in this paper. Several future promising research opportunities are described in this paper to provide insight to other researchers to dig deeper into this research field.

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Section: The Nlos and Multipath Environmentmentioning

confidence: 99%

Future Research and Trends in Ultra-Wideband Indoor Tag Localization

Hapsari,

Munadi,

Erfianto

et al. 2024

IEEE Access

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“…The related work for localization systems is summarized in Table 1. Most studies on localization are based on twodimensional (2D) environments, and only a few are based on three-dimensional (3D) localization systems [8]- [16]. The UWB technology is widely used in indoor localization systems because of characteristics such as high definition, robust against multipath effect and can penetrate various obstacles.…”

Section: A Related Workmentioning

confidence: 99%

“…However, a few drawbacks are associ-A weighted least square system was proposed for 3D positioning that uses information such as time of arrival (ToA), time difference of arrival (TDoA), and direction of arrival (DoA). [8] An alternative positioning method for the trilateration method with lower computational complexity was proposed. [9] RSS, Kalman filter, SVD,…”

Section: Introductionmentioning

confidence: 99%

Deep Gated Recurrent Unit-Based 3D Localization for UWB Systems

2021

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The localization system has been extensively studied because of its diverse applicability, for example, in the Internet of Things, automatic management, and unmanned aerial vehicle services. There have been numerous studies on localization in two-dimensional (2D) environments, but those in three-dimensional (3D) environments are scarce. In this paper, we propose a novel localization method that utilizes the gated recurrent unit (GRU) and ultra-wideband (UWB) signals. For the purpose of this study, we considered that the UWB transmitter (Tx) and many UWB receivers (Rx) were placed inside a confined space. The input of the proposed model was generated from the UWB signals that are sent from the Tx to the Rxs, and the output was the location of the Tx. The proposed GRU-based model converts the localization problem into a regression problem by combining the ranging and positioning phase. Thus, the proposed model can directly estimate the location of the Tx. Our proposed GRU-based method achieves 15 and four times shorter execution times for the training and testing, respectively, compared to the existing convolutional neural network (CNN)-based localization methods. The input data can also be easily generated with low complexity. The rows of the input matrix are the downsampled version of the UWB received signal. Throughout numerous simulation results, our novel localization method can achieve a lower root-mean-squared error up to 0.8 meters compared to the recently proposed existing CNN-based method. Furthermore, the proposed method operates well inside a confined space with fixed volume but varying width, height, and depth. INDEX TERMS 3D localization; deep learning; gated recurrent unit (GRU); recurrent neural network (RNN); ultra-wideband (UWB) system.

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Improvement of source localization via cellular network using machine learning approach

Phruksahiran¹

2023

Telecommun Syst

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3D WLS hybrid and non hybrid localization using TOA, TDOA, azimuth and elevation

Cited by 10 publications

References 32 publications

Future Research and Trends in Ultra-Wideband Indoor Tag Localization

Future Research and Trends in Ultra-Wideband Indoor Tag Localization

Deep Gated Recurrent Unit-Based 3D Localization for UWB Systems

Improvement of source localization via cellular network using machine learning approach

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