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Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-lin...
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... The researchers have proposed various techniques to mitigate the effects of multipath in 5G positioning. These include using advanced signal processing techniques, such as adaptive filtering and channel equalization, to suppress or eliminate multipath interference [105]. ...
5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.
... The researchers have proposed various techniques to mitigate the effects of multipath in 5G positioning. These include using advanced signal processing techniques, such as adaptive filtering and channel equalization, to suppress or eliminate multipath interference [105]. ...
5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.
... Location-based information services have been used in many applications, such as location-based bicycle sharing, route planning for daily travel, industrial Internet applications, emergency rescue services, road traffic control, and autonomous driving technology. Considering the increasing demand for location-based services, mobile communication technology-based location methods have gradually gained attention [1][2][3]. In recent years, with the continuous development of 5G technology [4], many studies based on 5G-related technology localization methods have emerged [5][6][7][8][9][10][11][12][13]. ...
The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.
... 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. ...
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.
... Location-based information services have been used in many applications such as location-based bicycle sharing, route planning for daily travel, industrial Internet applications, emergency rescue services, road traffic control, and autonomous driving technology. Considering the increasing demand for location-based services, mobile communication technology-based location methods have gradually gained attention [1][2][3]. ...
... It was assumed that the stage from m IRS reflected the signal to BS , which is referred to as the stage of m IRS BS − , and the signal propagation schematic of this phase is shown in Fig. Fig. 3. As shown in Fig. Fig. 3 , and its specific expression is expressed as (3). ...
The intelligent Reflective Surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain customized reflected wave direction by modulating the amplitude, phase, which can be easy deployed to change the wireless signal propagation environment and enhance the communication performance under Non-Line-of Sight (NLOS) environment where location services cannot be performed accurately. In this paper, a low-rank matrix reconstruction enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and thus receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as fingerprint to form a partial fingerprint database. As a next step, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve a sub-meter accuracy in a NLOS environment.
... The Time of Arrival (ToA)-based positioning is typically based on the correlation between a reference signal (here SRS or PRS) with the received signal r a 2 (t) [4,22,23]. Examples of correlation outputs under various wireless channel models are provided in The presence of NLOS in the wireless channel path deteriorates both the ToA and AoA estimates if the NLOS paths cannot be detected and eliminated from the final positioning solution [24,25]. Recent research in [26] has also proposed ways to harness information from NLOS paths in order to enhance the UE position and orientation information. ...
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.
... Koivisto et al. [32] demonstrated a passive and active location estimation scheme based on the 5G ultra-dense network and concluded that the accuracy and reliability of this scheme is well suited for applications in the transportation industry. To address the problem of large ranging errors in the non-visual propagation of the 5G, Deng et al. [33] proposed a time-delay estimation algorithm using mutual correlation to identify and cancel out non-visual signals. Simulation experimental results showed that this algorithm could markedly improve localization accuracy in urban canyon environments. ...
Global Navigation Satellite System provides real-time and all-weather positioning with high accuracy. Under a good observational environment, short-baseline real-time kinematic (RTK) can provide centimeter-level positioning results. However, RTK without model correction of ionospheric delay will significantly reduce the positioning accuracy, and cannot achieve fast and high-precision positioning when the baseline is too long or heavily occluded. Therefore, we proposed a combined RTK/fifth-generation (5G) mobile communication technology positioning model by combining global positioning system-RTK with the 5G time of arrival observations to improve the positioning accuracy under medium and long baselines. Experimental validation and analysis were conducted based on the measured data of different baseline lengths. Results revealed that the combined RTK/5G positioning model markedly improved the positioning performance in both static and dynamic modes under medium- and long-distance baselines. In particular, the RTK/5G model can also achieve good positioning results in conditions where some satellites are occluded. The combined RTK/5G positioning model is important for achieving high accuracy and real-time and continuous positioning in complex environments.
... 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]. Studies have also shown that positioning 5G base stations on both sides of the expressway can improve the robustness and accuracy of the car navigation system on the road [25]. ...
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.
