Experimental Evaluation of Round-Trip ToF-based Localization in the 60 GHz Band

Maletić, Nebojša; Sark, Vladica; Ehrig, Marcus; Gutiérrez, Jesús; Grass, Eckhard

doi:10.1109/ipin.2019.8911743

Cited by 13 publications

(14 citation statements)

References 21 publications

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“…In particular, to estimate the location, these techniques capitalize on the time measurements carried out between the agents, i.e., MUs and APs, requiring them to have a common time base [3]. Therefore, for the cooperative approaches to function, the APs need to be accurately synchronized among each other as well as with MUs [4,5].Considerable effort has been made to design fast, continuous, and precise synchronization algorithms across different networks, from Wireless Sensor Networks (WSNs) to wireless communication networks [6]. Generally, state-of-the-art synchronization Abstract Clock synchronization has always been a major challenge when designing wireless networks.…”

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confidence: 99%

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Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

Goodarzi

Cvetkovski

Maletić

et al. 2021

J Wireless Com Network

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Clock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

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confidence: 99%

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confidence: 99%

Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

Goodarzi

Cvetkovski

Maletić

et al. 2021

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the implemented system provides the absolute dc values for corresponding displacements, the polarity of the measured dc values can be predicted by tracking the previous dc values. For example, when the target transmitter is moved from (0, 15) to (2,12), the measured V dc1 increases, but the V dc2 decreases, as the transmitter is moving away from R x2 and approaching R x3 . On the other hand, when Tx is positioned at (4,11),absolute V dc1 increases, while absolute V dc2 also increases, rather than decreasing, so the polarity of V dc2 is inverted, as highlighted in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Normally, Time of Arrival (TOA) or Time Difference of Arrival (TDOA) measurements are used to calculate the required distances of the wireless transmitter from the reference receivers [8]. Some implemented systems use round trip time of flight (RTOF) [12] for object localization. Angulations techniques use relative angle information from the reference receivers to estimate object's position [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Nawaz

Tahir

Ahmed

et al. 2021

Entropy

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Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

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“…A strong amalgamation of GPS and CDMA can remarkably decrease hardware complexity. An additional TOA-AOA hybrid location spotting method relies on multilayer neural networks [37].…”

Section: A Smart Antenna Techniques For Localizationmentioning

confidence: 99%

Network Aided Positioning Techniques Based on Rssi and Signal Processing

Kevin¹

2020

ijarcs

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The researcher's attentiveness has been diverted to create mobile positioning techniques based on RSSI and with signal processing techniques like GPS and multidimensional scaling. The localization system has been proposed in different topologies with a lot of tactical challenges in mobile positioning. Several positioning systems has been proposed for mobile based wireless sensor network and some are in a dense environment. In consequence, it is not practical to use a same localization system for different types of networks. Therefore, a system and algorithm are required which can calibrate itself and transform the global coordinates according to the environmental changes. In this article we provide an overview of different localization systems and provide some insight overview for future researchers.

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Experimental Evaluation of Round-Trip ToF-based Localization in the 60 GHz Band

Cited by 13 publications

References 21 publications

Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Network Aided Positioning Techniques Based on Rssi and Signal Processing

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