Jun 2026· Journal of Physics, Conference Series· Vol 3272, pp. 012024· 0 citations· 10 references
Physics
TL;DR
The design integrates a low-noise amplifier, a high-linearity mixer, and an adaptive automatic gain control mechanism within a jointly optimized RF front-end architecture, aiming to improve signal quality at the receiver input under weak-signal conditions.
Abstract
Global Navigation Satellite Systems (GNSS) are widely used in intelligent transportation systems (ITS) and autonomous driving. However, in urban canyons and tunnels, multipath, blockage, and interference reduce SNR and degrade positioning accuracy. This paper proposes a high-SNR GNSS receiver architecture based on RF front-end optimization. The design integrates a low-noise amplifier (LNA), a high-linearity mixer, and an adaptive automatic gain control (AGC) mechanism within a jointly optimized RF front-end architecture, aiming to improve signal quality at the receiver input under weak-signal conditions. Experimental results show an SNR improvement of approximately 6 dB compared with a conventional receiver, along with improved positioning accuracy under both open-sky and weak-signal conditions.
(English) Achieving robust positioning across ground and UAV platforms remains challenging under multipath, partial satellite visibility, and rapidly changing measurement quality, especially in urban and embedded scenarios. At the same time, modern smartphones and embedded receivers increasingly provide multi-constellation, dual-frequency observations, carrier-phase measurements, and IMU streams that can be exploited for aided positioning.
The present thesis addresses these conditions through a robust GNSS/IMU integration framework, evaluated across heterogeneous sensor grades, from navigation-grade platforms to consumer smartphones. The framework is designed not only for offline post-processing, but also for real-time and embedded operation, following a deterministic execution structure suitable for on-board use.
The principal conclusions are:
First, a unified processing framework has been developed for GNSS/IMU integration using raw, undifferenced, uncombined GNSS observables (code, carrier phase, Doppler) and inertial measurements.
Second, a Square-Root Information Filter (SRIF) architecture has been adopted as the estimator core, enabling a numerically robust implementation and a shared software structure across GNSS-only processing, loosely coupled (LC) fusion, and tightly coupled (TC) fusion modes.
Third, Allan deviation analysis has been used systematically to identify inertial noise parameters and to configure the process-noise model of the navigation filter across different IMU classes, improving consistency of tuning when detailed manufacturer specifications are incomplete.
Fourth, experimental validation on multiple independent datasets, spanning different GNSS conditions and equipment grades, shows that LC fusion provides the most consistent practical gains, especially in continuity and robustness during short GNSS degradations, and often improves typical solution behaviour when inertial data quality is adequate.
Fifth, in dense urban conditions with strong multipath and masking, positioning performance remains fundamentally constrained by GNSS measurement quality and correction level; inertial aiding mitigates short-term disruptions but does not eliminate the GNSS-side error ceiling.
Finally, the thesis demonstrates a transferable and numerically stable GNSS/IMU integration framework, a reproducible Allan-based methodology for configuring heterogeneous IMUs in the filter, and a realistic path toward robust positioning under practical field conditions.
(Català) Assolir un posicionament robust en plataformes terrestres i UAV continua sent un repte en presencia de multipath, visibilitat satel·lital parcial i canvis rapids en la qualitat de les observacions, especialment en escenaris urbans i embeguts. Al mateix temps, els telefons intel·ligents i els receptors embeguts moderns proporcionen cada cop mes observacions multiconstel·lacio i de doble frequencia, mesures de fase portadora i fluxos IMU aprofitables per al posicionament assistit.
La present tesi aborda aquestes condicions mitjancant un marc robust d'integracio GNSS/IMU, avaluat en sensors de diferents graus, des de plataformes de grau de navegacio fins a telefons intel·ligents de consum.
El marc esta concebut no nomes per al post-processament fora de linia, sino tambe per a operacio en temps real i en sistemes encastats, seguint una estructura d'execucio determinista adequada per a aquest tipus de desplegament.
Les conclusions de la recerca son:
Primer, s'ha desenvolupat un marc unificat de processament per a la integracio GNSS/IMU utilitzant observables GNSS crus, no diferenciats i no combinats (codi, fase portadora i Doppler) i mesures inercials.
Segon, s'ha adoptat una arquitectura de Filtre d'Informacio en Arrel Quadrada (SRIF) com a nucli de l'estimador, la qual cosa permet una implementacio numericament robusta i una estructura de programari compartida per al processament GNSS-only, la fusio en acoblament lax (LC) i la fusio en acoblament estret (TC).
Tercer, l'analisi de desviacio d'Allan s'ha utilitzat de manera sistematica per identificar parametres de soroll inercial i per configurar el model de soroll de proces del filtre de navegacio en diferents classes d'IMU, millorant la coherencia de l'ajust quan les especificacions del fabricant son incompletes.
Quart, la validacio experimental en multiples conjunts de dades independents, que abasten diferents condicions GNSS i graus d'equipament, mostra que la fusio LC aporta les millores practiques mes consistents, especialment en continuitat i robustesa davant degradacions breus de GNSS, i sovint millora el comportament tipic de la solucio quan la qualitat de les dades inercials es adequada.
Cinque, en entorns urbans densos, amb multipath intens i emmascarament, el rendiment del posicionament continua limitat de manera fonamental per la qualitat de la mesura GNSS i el nivell de correccions; l'ajuda inercial mitiga interrupcions de curt termini, pero no elimina el sostre d'error del costat GNSS.
Finalment, en conjunt, la tesi demostra un marc d'integracio GNSS/IMU transferible i numericament estable, una metodologia reproduible basada en Allan per configurar IMUs heterogenis al filtre, i una via realista cap a un posicionament robust en condicions de camp.
(Español) Lograr un posicionamiento robusto en plataformas terrestres y UAV sigue siendo un reto en presencia de multitrayectoria, visibilidad satelital parcial y cambios rapidos en la calidad de las observaciones, especialmente en escenarios urbanos y embebidos. Al mismo tiempo, los telefonos inteligentes y receptores embebidos modernos proporcionan cada vez mas observaciones multiconstelacion y de doble frecuencia, medidas de fase portadora y flujos IMU aprovechables para posicionamiento asistido.
La presente tesis aborda estas condiciones mediante un marco robusto de integracion GNSS/IMU, evaluado en sensores de distintos grados, desde plataformas de grado navegacion hasta telefonos inteligentes de consumo. El marco esta concebido no solo para post-procesado fuera de linea, sino tambien para operacion en tiempo real y en sistemas embebidos, siguiendo una estructura de ejecucion determinista adecuada para uso a bordo.
Las principales conclusiones son:
Primero, se ha desarrollado un marco unificado de procesamiento para la integracion GNSS/IMU utilizando observables GNSS brutos, no diferenciados y no combinados (codigo, fase portadora y Doppler) y medidas inerciales.
Segundo, se ha adoptado una arquitectura de Filtro de Informacion en Raiz Cuadrada (SRIF) como nucleo del estimador, lo que permite una implementacion numericamente robusta y una estructura software compartida para el procesamiento GNSS-only, la fusion en acoplamiento laxo (LC) y la fusion en acoplamiento estrecho (TC).
Tercero, el analisis de desviacion de Allan se ha utilizado de forma sistematica para identificar parametros de ruido inercial y para configurar el modelo de ruido de proceso del filtro de navegacion en distintas clases de IMU, mejorando la coherencia del ajuste cuando las especificaciones del fabricante son incompletas.
Cuarto, la validacion experimental en multiples conjuntos de datos independientes, que abarcan distintas condiciones GNSS y grados de equipamiento, muestra que la fusion LC aporta las mejoras practicas mas consistentes, especialmente en continuidad y robustez ante degradaciones breves de GNSS, y a menudo mejora el comportamiento tipico de la solucion cuando la calidad de los datos inerciales es adecuada.
Quinto, en entornos urbanos densos, con multipath intenso y enmascaramiento, el rendimiento del posicionamiento sigue limitado de forma fundamental por la calidad de la medida GNSS y el nivel de correcciones; la ayuda inercial mitiga interrupciones de corto plazo, pero no elimina el techo de error del lado GNSS.
Por ultimo, en conjunto, la tesis demuestra un marco de integracion GNSS/IMU transferible y numericamente estable, una metodologia reproducible basada en Allan para configurar IMUs heterogeneos en el filtro, y una via realista hacia un posicionamiento robusto en condiciones de campo.
Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of R95 = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications.
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The Global Navigation Satellite System (GNSS) has been widely adopted in navigation applications due to its high accuracy and convenience. However, in urban canyon environments, severe signal blockage caused by buildings and trees introduces substantial non-line-of-sight errors and multipath effects, leading to degraded and highly fluctuating positioning performance. To address this issue, this paper proposes a residual-guided hybrid stochastic modeling framework. The method operates in two stages: first, a pseudo-range correction estimation network takes GNSS parameters containing residuals as input to estimate pseudo-range correction; second, these estimated corrections together with elevation angle and carrier-to-noise ratio are fed into a hybrid stochastic model parameter estimation network to determine model parameters. This design adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios. Meanwhile, the explicit modeling of relationships among pseudo-range correction, elevation angle, and carrier-to-noise ratio renders the stochastic model parameters interpretable. Experiments on public urban GNSS datasets demonstrate that the proposed method achieves competitive positioning performance against both conventional and learning-based baselines. It delivers notable accuracy improvements in light urban canyon environments, particularly on the KLT2 sequence, while maintaining robust and competitive performance in the more challenging TST and Mong Kok scenarios. These results validate the effectiveness of jointly estimating pseudo-range corrections and adaptive observation weights for enhancing positioning accuracy and robustness across diverse urban environments.
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