A Map-Free Indoor Localization Method Using Ultrawideband Large-Scale Array Systems

Ji, Yilin; Hejselbak, Johannes; Fan, Wei

doi:10.1109/lawp.2018.2863291

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…The computational complexity of Algorithm 1 can be analyzed in two stages. First, the initialization stage requires the computation of the LS solution in (34), which can be handled very efficiently since the matrices…”

Section: Complexity Analysismentioning

confidence: 99%

“…In [34], a map-free indoor localization method using ultrawideband (UWB) mm-wave signals is proposed, utilizing the estimated scatterer positions of the environment as virtual anchors to obtain the UE location. However, a snap-shot approach is taken, thus ignoring the correlation in sequences of measurements obtained by moving sensors.…”

mentioning

confidence: 99%

“…In [38], a JCAS system is pursued for smart home scenarios, investigating different integration approaches for traditional sensing and WiFi devices. In general, due to the notable challenges in performing RF measurements at mm-wave bands, only very few works have supported their positioning/sensing/mapping solutions with empirical results to assess and demonstrate the performance with real mm-wave transceiver and antenna hardware [25], [34], [39]- [41].…”

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confidence: 99%

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Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Barneto

Rastorgueva-Foi

Keskin

et al. 2022

IEEE Trans. Veh. Technol.

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Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radiobased sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient ℓ1-regularized least-squares (LS) approach to obtain sparse range-angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks.

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Section: Complexity Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Barneto

Rastorgueva-Foi

Keskin

et al. 2022

IEEE Trans. Veh. Technol.

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“…For example, many applications such as smart agriculture [1] [2], smart robot [3] [4], environmental monitoring [5] [6], require accurate location information of the target node. The UWB (Ultra wideband) positioning system [7] [8] [9] transmits data by sending narrow pulses at the nanosecond level, which can provide centimeter-level ranging accuracy. At the same time, the genetic algorithm [10] [11] [12] [13] [14] has become a common method due to its high solution accuracy, fast convergence speed, and strong robustness among the positioning algorithms.…”

Section: Introductionmentioning

confidence: 99%

UWB Positioning System Based on Genetic Algorithm

Xia¹,

Zheng²,

Zhang³

et al. 2021

JCC

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In order to enhance the positioning accuracy, a UWB positioning system based on genetic algorithm is proposed. Firstly, it uses the DW1000 module to measure the distance, and preprocesses the measured data to remove noise. Then, a positioning equation is established according to the processed distance information, and the genetic algorithm is used to solve the equation to obtain the coordinates of the unknown node. The experimental results show that the measured positioning accuracy is within 30 cm, which means that the system can obtain a good positioning effect and meet the need for precise positioning.

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“…The advent of Internet-of-Things (IoT) has revolutionized the field of telecommunications opening the door for unprecedented applications such as [1]- [9]. These applications are enabled through smart sensors that cooperate efficiently to provide the desired service.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

AlHajri¹,

Ali²,

Shubair³

2019

Antennas Wirel. Propag. Lett.

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Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments.Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.

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A Map-Free Indoor Localization Method Using Ultrawideband Large-Scale Array Systems

Cited by 11 publications

References 9 publications

Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

UWB Positioning System Based on Genetic Algorithm

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

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