Satellite Signals Research Articles

An Improved Low-Cost Dual-Antenna GNSS Dynamic Attitude Determination Method in Complex Environments

GNSS attitude determination has been widely adopted due to its high efficiency, absence of cumulative errors, and ease of installation. However, practical navigation and attitude determination systems often rely on low-cost receivers that struggle with substantial multipath effects, frequent cycle slips, and satellite signal loss, significantly impairing attitude determination accuracy in challenging urban environments. To address this issue, this contribution proposes a constrained dynamic prediction model (C-Dynamics), which enables more accurate initial coordinates and thereby increases the effectiveness of the constrained LAMBDA (CLAMBDA) technique. To evaluate the practical performance of C-Dynamics, two sets of real-world data collected from a vehicle platform were analyzed. The results demonstrate that C-Dynamics significantly enhances the accuracy of initial coordinate estimations across various environments. Compared with the lambda method, the CLAMBDA method + C-Dynamics method (CLAMBDA+CD) improves the fixing rate in the urban environment by 5.6%, and the accuracy of the heading angle, pitch angle, and baseline length improved by 66%, 70.9%, and 84.2%, respectively. Moreover, in challenging high obstruction environments, the fixing rate increased by 43.5%, while the accuracy of heading angle, pitch angle, and baseline length improved by 76.4%, 69.2%, and 94%, respectively. The proposed algorithm effectively addresses the low fixing rate and insufficient accuracy of the LAMBDA method in high obstruction environments and holds practical value for widespread adoption in the mass market.

Determination of zooplankton absorption spectra and their potential contribution to ocean color

Zooplankton are keystone organisms that provide a critical link between primary production and higher-order predators in the marine food web, as well as facilitating the sequestration of carbon within the ocean. In this context, there is considerable interest in the detection of zooplankton swarms from satellite ocean color signals. However, for this to be possible, accurate inherent optical property characterization of key zooplankton groups is first required. In this study, spectral absorption properties of six epipelagic zooplankton groups have been measured using what we believe to be a novel serial addition technique carried out with a Point Source Integrating Cavity Absorption Meter. The measured absorption spectra were used to model the impact of each group on remote sensing reflectance signals and determine a concentration threshold that would generate a distinguishable signal from ocean color data. Results indicate that the spectral shape of absorption did not vary much between species, with most organisms showing a peak at around 480 nm, characteristic of the pigment astaxanthin. Conversely, the magnitude of absorption did vary considerably between species, with larger organisms typically producing stronger absorption signals than smaller species. Thus, detection thresholds also varied for each group measured and were additionally influenced by background constituents within the water column. The calculated concentration thresholds indicate the feasibility of identifying zooplankton from ocean color, but owing to the spectral similarity in absorption properties, knowledge of in situ populations would be required to determine species abundances from satellite signals.

Urban Building Updates Monitoring Based on Sky Visibility Estimation From Satellite Signals

Urban Building Updates Monitoring Based on Sky Visibility Estimation From Satellite Signals

Enhancing Satellite Link Security Against Drone Eavesdropping Through Cooperative Communication

ABSTRACTIntegrated satellite terrestrial networks (ISTNs) are emerging as a promising next‐generation communication technology, for example, B5G and 6G, with low‐earth orbit (LEO) satellites playing a growing role. However, the complex and unique characteristics of ISTNs make them more susceptible to cyberattacks. Recently, the use of drones for public and private services has increased the risk of eavesdropping on LEO satellite links. Such scenario presents an extremely challenging environment due to dynamic nature of LEO satellite and drone along with atmospheric attenuation at sub‐THz frequencies. This study proposes a novel adaptive power‐bandwidth cooperative scheme designed to mitigate the likelihood of eavesdropping attacks on LEO satellite links communicating with a ground station when a drone is within the line of sight. The mathematical algorithm dynamically adapts the resources to maximize the normalized secrecy capacity in this challenging scenario while maintaining a reasonable signal‐to‐noise ratio (SNR) at the legitimate receiver. The adaptive scheme involves strategic cooperation with a nearby terrestrial third party to amplify and forward the satellite signal to the ground station receiver. The simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements (> 70%) compared to the non‐adaptive scheme over a wide range of elevation angles.

Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.

Assessing the ionospheric scintillations occurrence on L-band in the southern Mediterranean sector

We assess the ionospheric scintillation occurrence on Global Navigation Satellite Signals (GNSSs) over the Mediterranean sector under the rising phase of the current solar cycle. To the scope, we leverage on a network of three Ionospheric Scintillation Monitoring Receivers (ISMRs), being part of the electronic Space Weather upper atmosphere (eSWua: eswua.ingv.it) system (Pica et al., 2021). Such ISMRs are located in Lampedusa Island (Italy, Lat: 35.52°N–Lon: 12.63°E), Chania (Crete, Greece, Lat: 35.52°N, Lon: 24.04°E) and Nicosia (Cyprus, Lat: 35.18°N–Lon: 33.38°E). To our knowledge, this is the first thorough assessment of the scintillation patterns in the Southern Mediterranean sector, aimed at depicting how small-scale irregularities that form in the area can potentially affect the GNSS-based positioning and related application and services. We analyse the period from January 2021 to December 2023, reporting that the bulk of the scintillation occurrence is due to small-scale irregularities forming in the southernmost area of the field of view of the network. Irregularities of such a scale are formed during the evolution of the Rayleigh-Taylor instability featuring the Equatorial Plasma Bubbles (EPB), which may spill-over in the field of view (FoV) of the receivers, i.e. at low elevation angles in the southernmost azimuthal range. As observations at low elevation angles are subject to multipath mimic weak to moderate scintillation conditions, we focus exclusively on severe amplitude scintillation occurrence (S4 > 0.7) in the azimuthal range 110–250°, w.r.t the FoV of the receivers, subsequently reaching down to the Saharian ionosphere. To further confirm the nature of the detected GNSS scintillation occurrence, we compare the results with the Swarm Level-2 Ionospheric Bubble Index (IBI) evaluated within the same period. In the context of the April 2023 geomagnetic storm, a worst-case scenario is also documented, illustrating the potential impact of ionospheric disturbances associated with EPBs in the Southern Mediterranean area.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

A Cumulant-Based Method for Acquiring GNSS Signals.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo-developed and maintained by the European Union-utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs.

Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

An improved multiple-outlier-robust GNSS/INS EKF filer based on multiple statistical similarity measure

Abstract High-precision and highly reliable location information is the key support for autonomous driving applications. Global Navigation satellite system (GNSS)/Inertial navigation system (INS) integrated positioning is a typical high-precision positioning technology. However, satellite signals are easily affected by various interferences such as signal blocking and multipath problems, leading to performance degradation in complex urban environments. The traditional extended Kalman filtering (EKF) cannot handle observation outlier, and the system’s positioning accuracy sharply declines when the observations are affected by non-Gaussian heavy-tailed noise. Therefore, this paper presents an improved multiple-outlier-robust extended Kalman filter (I-MOREKF) for GNSS/INS Loosely Coupled Integration. A multiple statistical similarity measure (MSSM) is built to evaluate the similarity between two random vectors from dimension to dimension. Then, the I-MOREKF is proposed by maximizing a cost function based on the MSSM, and considering the robust estimation with IGG III model. The proposed model effectively removes multiple types of errors in complex environments. A vehicular test was carried out to validate the feasibility and performance of the proposed model. Simulated random gross errors are introduced in GNSS measurements, and the measurement noises are corrupted by heavy-tail noise. The results show that simulated gross errors can be successfully detected with I-MOREKF model, and the 3D position Root Meas Square Error is 0.0148 m for I-MOREKF, which are improved by 97.2%,13.5% and 52.9%, as compared with EKF, IGG Ⅲ and MOREKF.

Study on the detection of power line operation anomalies in the Hexi Corridor based on multi-scale filtering of BeiDou satellite signals

In the artificial intelligence detection of power line anomalies, the identification effect is determined by the positioning process of two-dimensional map, which is also a difficult point in this field Therefore, this paper proposes a method to detect the abnormal operation of power lines in Hexi Corridor based on multi-scale filtering of Beidou satellite signals. Multi-scale processing is carried out by wavelet transform to obtain multiple wavelet basis functions of different scales. On this basis, a two-dimensional time–frequency diagram is drawn according to the results of continuous wavelet transform. The adaptive multi-time frequency entropy method is selected to divide the two-dimensional time–frequency map into multiple layers and scales, and the adaptive multi-scale time–frequency entropy of the time–frequency plane is calculated. After smoothing the obtained power line position signal by S-G filtering method, the power line position signal of Hexi Corridor is obtained by GPS/ Beidou dual-mode satellite positioning system, and the positioning signal is optimized by using the positioning mechanism of multiple receiving stations. Realize the abnormal operation detection of power lines in Hexi Corridor. The experimental results show that this method can obtain more accurate positioning signals, and the geometric accuracy factor GDOP fluctuates in the range of 0 ∼ 2. The unsmooth signal can be effectively removed; The maximum error result of abnormal position detection is 1.22kn；; At the same time, the abnormal displacement section of the line can be determined.

A deep learning GNSS spoofing and jamming detection method with dual-frequency C/N0 heatmap

Abstract The Global Navigation Satellite System (GNSS) is vulnerable to interference due to the open signal structure and low signal strength, posing a significant threat to the billions of terminals worldwide that rely on GNSS receivers for precise Positioning, Navigation, and Timing (PNT) services. In this paper, we propose a cloud-edge framwork for GNSS spoofing and jamming monitoring, comprising the data acquisition module, GNSS monitoring module, detecting and reporting module. In this framwork, we design a Deep Learning (DL) method for detecting GNSS interference through Dual-frequency Carrier-to-Noise density ratio (C/N0) heatmaps (DD-C/N0). This method involves extracting and correlating features from C/N0 heatmaps of visible navigation satellites operating in the GPS L1 and L2 frequency bands, allowing the identification of anomalous patterns. A U-BLOX receiver was utilized to capture the GNSS satellite signals, while commercial jammers and Software-Defined Radio (SDR) HackRF One kits were employed to simulate the interference sources. Experimental results demonstrate that the proposed method achieves significantly higher performance, with an accuracy of 99% and 98% on the public dataset and real-time testing data, compared to unsupervised, semi-supervised, and supervised detectors that rely solely on single-channel data (L1 frequency band). Integrated with the DD-C/N0 method, the online GNSS monitoring system will be improved and deployed to automate spoofing and jamming detection tasks in the next step.

LEO navigation observables extraction using CLOCFC network

In case of mitigate the reliance of aviation users on the Global Navigation Satellite System (GNSS) in an increasingly interference-prone environment, utilizing opportunistic signals from Low-Earth Orbit (LEO) for navigation and positioning is an alternative approach. However, LEO satellite SOPs are not intended for navigation. Therefore, it is necessary to design methods to extract navigation observables from these signals. In this paper, we proposed a lightweight deep learning model with a two-branch structure called CLOCFC, designed to extract navigation observables. Furthermore, we have established a low Earth orbit satellite signal dataset by using ORBCOMM constellation signals as the input to the model and Doppler frequency as the label for the model. The results show that CLOCFC, as a lightweight model, demonstrates a significantly faster convergence rate and higher accuracy in navigation observables extraction compared to other models (ResNet, Swin Transformer, and Clo Transformer). In CLOCFC, we introduce the CFC module, a kind of Liquid Neural Network, to enhance the information acquisition capability through the spatiotemporal information in the data sequence. Finally, we have also conducted extensive experiments with the Doppler shift extraction of LEO satellites as an example, under various noise and resolution conditions, demonstrating the superiority of the CLOCFC.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

A Study on Rainfall Rate, Temperature and Rain Attenuation of Ka-Band Satellite signal

This research paper is studied rainfall, temperature and rain attenuation of Ka-Band satellite signal by using rainfall and temperature data in 2023. Department of Electronics and Telecommunications Engineering, Faculty of Engineering is where the station is housed. Therefore, this paper aims to investigate the relationship between rainfall values, temperature and rain attenuation in Vientiane Capital using advanced technologies to facilitate and monitor the data in real time. A rain gauge Tipping Bucket 0.1mm/Tip is used to detect the amount of rain, and a DHT22 sensor is used to monitor the temperature. The research procedure entails analyzing temperature and rainfall, designing and setting up measurement sites, setting up a central monitoring station and using the Internet of Things to collect temperature and rainfall data. The analysis showed that the high precipitation amount of the actual rainfall and actual daily data contains two maximum values from August 15, 2023 at 85.3mm/hr with 26°C and September 7, 2023 at 110mm/hr with 25.1°C. ITU-R model is used to analyze the rain attenuation by applying the abovementioned rainfall values. As a result, the rain attenuation of Ka-Band Satellite Signal is 85.3mm/hr the rain attenuation is 11.63 dB and 110mm/hr the rain attenuation is 10.58 dB, respectively, which showed the interlinkage between rainfall amount and temperature while it is raining. The temperature change will not affect the rain attenuation as much as the increase of rainfall amount.

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

3D Positioning of Drones through Images.

Drones traditionally rely on satellite signals for positioning and altitude. However, when in a special denial environment, satellite communication is interrupted, and the traditional positioning and height determination methods face challenges. We made a dataset at the height of 80-200 m and proposed a multi-scale input network. The positioning index RDS achieved 76.3 points, and the positioning accuracy within 20 m was 81.7%. This paper proposes a method to judge the height by image alone, without the support of other sensor data. One height judgment can be made per single image. Based on the UAV image-satellite image matching positioning technology, by calculating the actual area represented by the UAV image in real space, combined with the fixed parameters of the optical camera, the actual height of the UAV flight is calculated, which is 80-200 m, and the relative error rate of height is 18.1%.

Crowdsourced Geospatial Intelligence: Constructing 3D Urban Maps with Satellitic Radiance Fields

In urban planning and research, 3D city maps are crucial for activities such as cellular network design, urban development, and climate research. Traditionally, creating these models has involved costly techniques like manual 3D mapping, interpretation of satellite or aerial images, or the use of sophisticated depth-sensing equipment. In this work, we propose a novel approach to develop 3D urban maps by examining the influence of urban structures on satellite signals, using GPS records crowdsourced from hundreds of smartphones during everyday user movements. We introduce the concept of satellitic radiance fields (SaRF), a novel neural scene representation technique designed to capture the distribution of GPS signals in urban settings. SaRF employs a sparse voxel octree framework to depict voxel-centric implicit fields, capturing physical properties like the density of each voxel. This model is progressively refined using a differentiable ray-marching process, ultimately leading to the reconstruction of 3D urban maps. Our thorough experimental evaluation, which incorporates approximately 27.4 million GPS records, reveals an average reconstruction accuracy of 83.1% in six varied urban scenes.

An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.