Reception and Processing of Signals in Prospective Models of User Navigation Equipment for GLONASS

Doppler-Based Localization Under Hardware, Atmospheric, and Orbital Impairments for Single LEO Satellite Systems

In order to enable a wide range of applications anywhere and anytime, future communication systems are expected to employ low Earth orbit satellites to perform user verification. Single-satellite systems offer a cost-effective verification alternative, reducing implementation complexity and dependence on satellite constellations. This article develops a single-pass, single-satellite localization algorithm independent from global navigation satellite systems, supporting user verification and requiring only coarse coverage-region side information. The algorithm is based on the tracking of phase changes from received pilot signals originated from Doppler shifts, inherently related to the user's position. Our work addresses realistic channel and receiver conditions—encompassing carrier frequency offset, phase noise, and atmospheric propagation effects—while evaluating robustness against orbital perturbations, a combination that has not been jointly addressed in prior studies on Doppler-based localization. The proposed two-stage approach employs an extended Kalman filter for estimation of the referred phase shifts, followed by a weighted least squares solution. Algorithm performance is evaluated through simulations in terms of mean and $90{\text{th}}$ percentile distance error, together with the time to reach a 10-km error level, an accuracy benchmark discussed in verification studies by 3rd Generation Partnership Project (3GPP). Results demonstrate improved accuracy with respect to compatible Doppler-based baselines, with $90{\text{th}}$ percentile errors falling below the 10-km mark under the considered narrowband and line-of-sight conditions, suggesting that the method may support user verification. The time required to achieve such accuracy, particularly, may require a significant portion of the satellite's visibility window in strong phase noise conditions.

Robust positioning using hybridization of GNSS with other measurements of opportunity

(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.

Velocity-Aided Navigation in GNSS-Denied Environments

Highlights What are the main findings? An alternative method for aircraft/drone navigation is proposed when the usual SINS/GNSS methods do not work. Semi-natural experiments confirmed the accuracy of the alternative method at the level of accuracy of SINS/GNSS methods, especially at intervals of up to 10 min. What are the implications of the main findings? The proposed VAN method is quite simple, since it is implemented using MEMS technology. The proposed method can be used as a backup method for aircraft navigation. Abstract The Velocity-Aided Navigation (VAN) method for determining latitude, longitude, and altitude is proposed when global navigation satellite system (GNSS) signals are unavailable. Currently, GNSS receivers are the primary navigation systems that meet consumer demand for location accuracy. However, GNSS receivers are not autonomous. Strapdown inertial navigation systems (SINSs), unlike GNSSs, are autonomous. Their operating principle is based on double integration of accelerometer output signals. However, they have a significant drawback: SINS errors increase significantly over time. Two approaches are used to improve accuracy. The first involves using expensive, high-precision gyroscopes and accelerometers. The other involves correcting the SINS by integrating it with navigation systems built on physical principles different from those of the SINS. An alternative method, based on VAN and an inertial measurement unit (IMU), for determining navigation parameters is proposed and does not require double integration of accelerometer output signals. Analytical expressions for the errors of the new method are derived. Calculations showed that the errors of the new method are significantly smaller than those of the autonomous SINS. Experimental testing confirmed the calculation results and demonstrated that the errors of the new method are comparable to those of the SINS integrated with GNSS using a Kalman filter. The proposed alternative VAN method for determining latitude, longitude, and altitude can be used independently, as an alternative to GNSS for integration with the SINS, and can also serve as a backup navigation system.

N92-22012 MULTI-USER SATELLITE COMMUNICATIONS SYSTEM USING AN INNOVATIVE COMPRESSIVE RECEIVER

Determination of navigation parameters of dynamic objects based on adaptive sensor integration in intelligent transport systems

Purpose or research . The aim of the study is to develop and analyze the architecture of a local navigation subsystem for unmanned aerial and ground transport platforms based on adaptive integration of heterogeneous sensor data. The research focuses on improving the accuracy of estimating navigation parameters under conditions of degraded or unavailable satellite navigation signals. Methods . The methodological basis includes system analysis, probabilistic state-estimation methods, dynamic and observation models, and adaptive filtering approaches. To combine data from inertial measurement units, satellite navigation systems, and coordinate sources of an intelligent transport infrastructure, the extended Kalman filter and particle filter are employed, with automatic switching based on the statistical analysis of measurement residuals. Results . An architecture of the positioning subsystem is formulated, incorporating data synchronization, preprocessing, and an adaptive algorithmic core. It is demonstrated that switching to the particle filter when normalized residuals exceed confidence thresholds makes it possible to compensate for GNSS outliers and temporary signal interruptions. Simulation results show a reduction of the root-mean-square positioning error from 4.2 m to 2.1 m and a limitation of maximum deviations to 2.4 m. Various operating modes of the subsystem under different combinations of available navigation sources are analyzed. Conclusion . The proposed method of adaptive sensor integration enables reliable estimation of navigation parameters of dynamic objects and can be used within control systems of unmanned aerial platforms and intelligent transport complexes. The architecture and algorithmic solutions provide the required accuracy and fault tolerance when operating in challenging urban environments.

A differential positioning method based on 5G and BeiDou satellites in complex environments

Signal obstruction in complex environments hinders receivers from acquiring sufficient and effective satellite signals, thus limiting positioning accuracy, stability, and availability. Based on the latest advances in satellite navigation and 5G communication technologies, this paper systematically investigates the theoretical approach to integrated positioning using the BeiDou System (BDS) and 5G. A seamless BDS+5G combined positioning method for complex urban environments is proposed. First, the mode switching conditions are determined by deeply integrating BeiDou and 5G observation information. Second, multidimensional parameters such as satellite elevation angle and CNR are comprehensively utilized to identify objects in complex obstructed environments. Finally, a dual-threshold discrimination mechanism is introduced to enhance the adaptability and robustness of positioning under varying signal conditions. The proposed model outperforms traditional BDS+5G combined models in dynamic obstruction. Experimental results demonstrate that the proposed model fully leverages the complementary characteristics of BeiDou and 5G in both outdoor obstruction and indoor-outdoor transition scenarios, achieving positioning accuracy of 1m horizontally and 2m vertically, significantly improving positioning accuracy and consistency.