As the need for more accurate and reliable positioning systems grows, satellite-based navigation techniques are gaining significant attention, particularly those utilizing Doppler shifts from Low Earth Orbit (LEO) satellites. Traditional Doppler positioning systems often suffer from errors induced by atmospheric disturbances, satellite clock biases, and other signal impairments, especially in dynamic environments. This has motivated the exploration of differential Doppler positioning as a promising solution to mitigate these common-mode errors. This paper explores the theoretical limits of differential Doppler positioning, focusing on Doppler-only methods where position and velocity estimates are derived from Doppler measurements without relying on time-of-arrival (TOA) measurements. By leveraging the Cramér-Rao lower bound (CRLB), we provide a theoretical performance benchmark for the accuracy of position, velocity, and frequency bias estimation. Furthermore, we present a correlation model for atmospheric effects to demonstrate the impact of baseline distance on the estimation performance of differential Doppler positioning. The results show that differential Doppler positioning notably outperforms traditional non-differential Doppler positioning, particularly in low-SNR environments, with substantial gains in frequency bias, 3D velocity, and 3D position estimation accuracy.

5G mmWave offers a high-precision positioning solution, functioning effectively in both line-of-sight (LoS) and operable non-line-of-sight (NLoS) conditions. However, in scenarios with complete signal blockage, integrating with motion-based models becomes crucial. This integration is achieved through Bayesian-based estimators, which entail a prediction and a correction stage weighted by their respective covariance matrices. Although covariance matrices of different prediction models have been extensively studied in the literature, the measurement covariance matrix derived from 5G-based position computations remains largely unexplored. In this paper, we propose a measurement covariance matrix tuning scheme based on a data-driven Cramér-Rao lower bound CRLB model. We validate the proposed algorithm within a simple linear Kalman filter (LKF) positioning framework. The methodology was tested in a controlled simulation scenario using a real 24-minute-long vehicular trajectory in a deep-urban environment. The results demonstrate that the developed data-driven model is reliable, maintaining a standard deviation error of less than 7 cm for 95% of the time and less than 0.5 m for 100% of the time relative to the true computed CRLB. The proposed adaptive KF sustains a position error below 30 cm for 99.3% of the time.

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing line-of-sight (LoS) 5G signals with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G single-bounce reflections (SBRs) and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with a low-cost inertial measurement unit (IMU) and a wheel encoder. Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized extended Kalman filter (EKF) within the 5G-based positioning research community. We performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were generated using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic NLoS and multipath scenarios. For the same trajectories, real motion data were collected from two different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when SBR signals are not considered, which is only around 92% of the time.

Achieving the highest levels of autonomy within autonomous vehicles (AV s) requires a precise and dependable positioning solution that is not influenced by the environment. 5G mm Wave signals have been extensively studied in the literature to provide such a positioning solution. Yet, it is evident that 5G alone will not be able to provide uninterrupted positioning services, as outages are inevitable to occur. Towards that end, few works have explored the benefits of integrating mm Wave positioning with onboard motion sensors (OBMS) like inertial measurement units (IMUs) and odometers. Inspired by INS-GNSS integration literature, all methods defaulted to a tightly-coupled (TC) integration scheme, which hinders the potential of such an integration. Additionally, the proposed methods were validated using simulated 5G and INS data with probability-based line-of-sight (LOS) assumptions. Such an experimental setup fails to highlight the true advantages and challenges of 5G-OBMS integration. Therefore, this study first explores a loosely-coupled (LC) 5G-OBMS integration scheme as a viable alternative to TC schemes. Next, it examines the merits and challenges of such an integration in a deep-urban setting using a novel quasi-real simulation setup. The setup comprises quasi-real 5G measurements from the Siradel simulator and real commercial-grade IMU measurements from a challenging one-hour-long trajectory in downtown Toronto. The trajectory featured multiple natural 5G outages which helped with assessing the integration's performance. The proposed LC method achieved a 14-cm level of accuracy for 95% of the time, while significantly limiting positioning errors during natural 5G outages.

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).

5G NR mm Wave promises accurate positioning down to the centimeter level. However, mmWave signals endure prismatic propagation, making them prone to signal blockages and non-line of sight (NLoS) communications. To achieve a precise positioning solution, it is rather essential to filter out NLoS gNBs as they yield erroneous pose estimation of IoT vehicular applications. Previous works have attempted to address this issue, however, they are either based on impractical or invalid assumptions about the operation scenario. In this paper, a novel, yet, simple and realistic NLoS detection algorithm is developed. The proposed method measures the discrepancy between Received Signal Strength (RSS)-based and time-based ranges as means to detect NLoS operation. To validate the proposed NLoS detection method, it was incorporated into a Kalman Filter (KF) that fuses the angle of departure (AoD) and round trip time (RTT) measurements from multiple gNBs in a loosely coupled fashion. The proposed method was evaluated on quasi-real measurements acquired from a highly validated 5G simulation tool that simulates the cores of downtown Toronto. The proposed method demonstrates superior results, as it sustains a sub-1m level of accuracy for around 95% of the time, as compared to merely 29% of the time without the NLoS detection.

5G small cells have the potential to enable sub-meter positioning accuracy in urban canyons and downtown areas, where global navigation satellite system (GNSS) precise point positioning (PPP) suffers the most. As 5G is expected to have a dense deployment of base stations (BSs), it became imperative to utilize the extra information available by means of sensor fusion. Traditionally, an extended Kalman filter (EKF) is used for such a purpose. Yet, one of its main drawbacks is that it requires a linear relationship between the states and the measurements to ensure its optimality. Many papers in the literature perform multi-BS hybrid positioning through the fusion of raw range-based and angle-based measurements via an EKF. Such measurements are inherently highly non-linear with respect to the estimated position state, which leads to high linearization errors. In this paper, we first propose the integration of the available BSs on the positioning level instead of the integration on the raw measurement level to avoid the linearization errors of the EKF. Additionally, we propose a dynamically tuned covariance matrix (DTCM)-KF method, where the BSs are weighted based on their proximity to the UEs, with BSs further away weighted less. The proposed method was tested using a quasi-real setup based on a highway trajectory in Toronto, Canada, along with a ray-tracing-based 5G simulator. The potential of using the proposed 5G positioning as an alternative to GNSS-based positioning in urban canyons is investigated through the comparison with the GPS PPP. The results show that the proposed method outperforms traditional EKF-based measurements level fusion methods. Moreover, it is able to outperform the GPS-only PPP solution. The RMS, maximum, and 95% errors of the proposed method were found to be 0. 39m, 1.4m, and 0. 74m respectively.