A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

Section: Signal Modelmentioning

confidence: 99%

Section: Signal Modelmentioning

confidence: 99%

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

“…Typically, it has been used in passive systems, such as passive radars and microphone arrays. Still, there are numerous challenges in TDOA estimation that must be addressed to meet the requirements for high accuracy and robustness of different applications [ 11 , 12 , 13 , 14 ]. In recent years, many TDOA algorithms have been proposed, each with strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Time Difference of Arrival Estimation Based on the Energy Center of Gravity Rearrangement

Zhang,

Wen,

et al. 2023

An accurate estimation of the time difference of arrival (TDOA) is crucial in localization, communication, and navigation. However, a low signal-to-noise ratio (SNR) can decrease the reliability of the TDOA estimation result. Therefore, this study aims to improve the performance of the TDOA estimation of dual-channel sensors for single-sound sources in low-SNR environments. This study introduces the theory of time rearrangement synchrosqueezing transform (TRST) into the time difference of arrival estimation. While the background noise TF points show random time delays, the signal time-frequency (TF) points originating from uniform directions that exhibit identical lags are considered in this study. In addition, the time difference rearrangement synchrosqueezing transform (TDST) algorithm is developed to separate the signal from the background noise by exploiting its distinct time delay characteristics. The implementation process of the proposed algorithm includes four main steps. First, a rough estimation of the time delay is performed by calculating the partial derivative of the short-time cross-power spectrum. Second, a rearrangement operation is conducted to separate the TF points of the signal and noise. Third, the TF points on both sides of the time-delay energy ridge are extracted. Finally, a refined TDOA estimation is realized by applying the inverse Fourier transformation on the extracted TF points. Furthermore, a second-order-based time difference reassigned synchrosqueezing transform algorithm is proposed to improve the robustness of the TDOA estimation by enhancing the TF energy aggregation. The proposed algorithms are verified by simulations and experiments. The results show that the proposed algorithms are more robust and accurate than the existing algorithms.

“…The use of 5G communication systems for indoor and outdoor positioning is a current research hotspot. Indoor and outdoor positioning algorithms based on the characteristics of 5G millimeter-wave signals have also been extensively studied, and the existing algorithms have reached sub-meter positioning accuracy [21][22][23]. Hybrid positioning schemes based on the fusion of 5G cellular, GNSS/INS are to be studied and developed towards a universal solution for robust positioning of aerial or ground vehicles in urban, rural, and indoor scenarios [24].…”

Section: Introductionmentioning

confidence: 99%

Simulation Experiment and Analysis of GNSS/INS/LEO/5G Integrated Navigation Based on Federated Filtering Algorithm

Wang

Zhao

Zhang

et al. 2022

This article examines the positioning effect of integrated navigation after adding an LEO constellation signal source and a 5G ranging signal source in the context of China’s new infrastructure construction. The tightly coupled Kalman federal filters are used as the algorithm framework. Each signal source required for integrated navigation is simulated in this article. At the same time, by limiting the range of the azimuth angle and visible height angle, different experimental scenes are simulated to verify the contribution of the new signal source to the traditional satellite navigation, and the positioning results are analyzed. Finally, the article compares the distribution of different federal filtering information factors and reveals the method of assigning information factors when combining navigation with sensors with different precision. The experimental results show that the addition of LEO constellation and 5G ranging signals improves the positioning accuracy of the original INS/GNSS by an order of magnitude and ensures a high degree of positioning continuity. Moreover, the experiment shows that the federated filtering algorithm can adapt to the combined navigation mode in different scenarios by combining different precision sensors for navigation positioning.