ToA ranging using subsample peak estimation and equalizer-based multipath reduction

Exel, Reinhard; Bigler, Thomas

doi:10.1109/wcnc.2014.6952928

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…Therefore, the ToA can be precisely estimated by using any STR algorithm, which are already included in most communication systems. Very similar solutions are described in other works [18] [21], where the receiver estimates the phase of the master clock and takes timestamps with sub-sample precision.…”

Section: Enhanced Timestampsmentioning

confidence: 93%

“…Advanced timestamping techniques have been proposed in the literature in order to obtain sub-nanosecond time synchronization over IEEE 802.11 [19], [20] and [21]. In [19], it is described an 802.11b implementation in FPGA with hardware timestamps and subsample timestamping precision.…”

Section: Background and Related Workmentioning

confidence: 99%

“…However, according to the experiments documented in [19], the performance of the system is highly deteriorated for time-variant channels, showing a synchronization error bigger than 20 ns. The receiver presented in [19] is improved in [20] and in [21] to combat the multipath propagation. The improvements proposed in [20] are mainly based on the use of frequency hopping in order to obtain timestamps considering several independent channels.…”

Section: Background and Related Workmentioning

confidence: 99%

“…However, such a system needs a very specific implementation and the authors state in the paper that the ranging error is significantly larger when the target is moving. The solution proposed in [21] uses the interpolation of the cross-correlation peak to take timestamps with subsample precision and an equalizer to combat the multipath components and reduce the ranging error. However, the system is still designed for LoS conditions, and it is vulnerable to strong multipath components.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Another wireless synchronization scheme based on the interpolation of the cross-correlation peak but over IEEE 802.15.4 Chirp Spread Spectrum (CSS) is shown in [22], [23] and [24]. In [21] it is developed a theoretical analysis of the feasibility of the CSS modulation to perform precise timestamping and an early experimental validation is presented. The system performance is reported through simulations under direct LoS and limited multipath propagation conditions.…”

Section: Background and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

Seijo

López-Fernández

Bernhard

et al. 2020

IEEE Trans. Ind. Inf.

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The Time-Sensitive networks paradigm envisions the integration of Operation Technology and Information Technology in the same network. One of the requirements for building Time-Sensitive networks is sharing a global time along the network. This requirement is especially critical in wireless systems, where there are few robust methods to perform accurate time transfer. In this paper, the problem of time transfer over realistic wireless channels is studied and a time distribution scheme is proposed. The time distribution scheme has three components: Precision Time Protocol, a novel timestamping method (enhanced timestamps) and an algorithm to implement the enhanced timestamps. The performance of the proposed scheme has been evaluated in MATLAB using the IEEE 802.11n standard under several standard Wireless Local Area Network channel models. The results show that the system can reach subnanosecond time transfer accuracy under Non-Line-of-Sight and time-variant conditions, but its performance greatly depends on the Signal-to-Noise-Ratio and on the channel variation rate.

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Section: Enhanced Timestampsmentioning

confidence: 93%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

Seijo

López-Fernández

Bernhard

et al. 2020

IEEE Trans. Ind. Inf.

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A Survey of Machine Learning for Indoor Positioning

et al. 2020

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Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

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Bluetooth Low Energy and CNN-Based Angle of Arrival Localization in Presence of Rayleigh Fading

Hajiakhondi-Meybodi

Salimibeni

Mohammadi

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently directionfinding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

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ToA ranging using subsample peak estimation and equalizer-based multipath reduction

Cited by 7 publications

References 14 publications

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

A Survey of Machine Learning for Indoor Positioning

Bluetooth Low Energy and CNN-Based Angle of Arrival Localization in Presence of Rayleigh Fading

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