Precision-Aided Partial Ambiguity Resolution Scheme for GNSS Attitude Determination

The use of carrier phase data play an important role for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). Similarly, precise orientation information can be obtained with multi-antenna setups which exploit carrier phase observables. The availability of high precision navigation solutions is, however, subject to the Integer Ambiguity Resolution (IAR) performance. IAR is the process of mapping the real-valued carrier ambiguities to integer ones, enhancing the attitude solution by virtue of the cross-correlation with the estimated integer ambiguities. Unfortunately, IAR is known to suffer from dimensionality course or, in other words, the chances for finding the correct vector of integers reduces with the number of ambiguities. This work focuses on improving the availability of high precision attitude estimates by means of using a Partial Ambiguity Resolution (PAR) scheme. PAR relaxes the condition of estimating the complete vector of ambiguities and, instead and finds a subset of them to maximize the availability. A new formulation for attitude determination using quaternion rotation within a precision-driven PAR scheme is proposed. Numerical simulations are used to showcase the attitude determination performance with a conventional Full Ambiguity Resolution (FAR) and a precision-aided PAR approach

Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique

A Collaborative RTK Approach to Precise Positioning for Vehicle Swarms in Urban Scenarios

Location information is fundamental in nowadays society and key for prospective driverless vehicles and a plethora of safety-critical applications. Global Navigation Satellite Systems (GNSS) constitute the main information supplier for outdoor positioning, with worldwide all-weather availability. While the use of GNSS carrier phase observations leads to precise location estimates, its performance can be easily jeopardized in urban scenarios, where satellite availability may be limited or observations may be corrupted by harsh propagation conditions. The satellite shortage is especially relevant for Real Time Kinematic (RTK), whose capability to estimate a precise positioning solution rapidly decays with weak observation models. To address this limitation, this article introduces the concept of collaborative RTK (C-RTK), an approach to precise positioning using swarms of vehicles, where a set of users participate in the vehicle network. The idea is that users with good satellite visibility assist users that evolve in constrained environments. This work introduces the C-RTK functional model, an estimation solution and associated performance bounds. Illustrative Monte Carlo simulation results are provided, which highlight that, by exploiting the cross-correlation terms present among the users' observations, C-RTK improves their positioning their of accuracy and availability

Robust indoor positioning in WLAN networks

Navigation and location technologies have been reaching in a major interest where Global Navigation Satellite System (GNSS) is mostly adopted. The limitation of this technology is that direct sky view is needed for reliable positioning. In indoor environments, however, it is difficult for GNSS technology to provide a reliable performance in positioning due to the signal attenuation and blocking caused by buildings and construction materials. For this reason, the growth in indoor applications has focused the research in new techniques for attempting mitigate the poor GNSS performance on this type of environments In the context of indoor positioning, multitude of emerging technologies for localization based on ultrasound, infrared, Ultra Wide Band (UWB), Zigbee, inertial navigation and other non-GNSS technologies have been proposed but special equipment is required and a large number of signal sources are needed. However, Wireless Local Area Network (WLAN) technology is widely used in indoor positioning. While the same requirements are also needed as the other technologies in order to improve the positioning accuracy, in terms of cost and ability, Wireless-based indoor location is widely used due to the already deployment of Anchor Points (AP) in urban and indoor areas. There are several methods for indoor positioning purposes e.g ToA (Time of Arrival), Received Signal Strength (RSS) measurements, AoA (Angle of Arrival), fingerprinting and so on. Most of the network-based location estimations use RSS measurements because almost all mobile devices are afforded to use this type of measurements. So, this thesis is centered in WLAN RSS-based positioning systems. The first step for indoor positioning is the distance estimation between the user and the AP. Theoretical and empirical propagation channel models are used to translate the difference between the transmitted and Received Signal Strength into an estimated range. A Propagation channel model built the radio map and also report changes in the environment. There are several models in the literature to characterize this channel. Indoor RSS-based localization has become a popular solution, but standard techniques still consider a time invariant simple single slope path loss channel model with a priori known constant channel parameters. While some contributions considered the RSS-based localization problem using a single path loss model with unknown parameters, the general solution that considersa generalized distance dependent measurement model is an important missing point. This thesis considers the two-slope channel model and proposes a robust indoor positioning solution based on a parallel architecture using a set of Interacting Multiple Models (IMM), each one involving two Extended Kalman filters (EKF) and dealing with the estimation of the distance to a given AP. Within each IMM, the two-slope path loss model parameters are sequentially estimated with Maximum Likelihood Estimate (MLE) to provide a robust solution. Finally, the set of distance estimates are fused into a standard EKF-based solution to mobile target tracking. In addition, the benchmarks derived in this thesis to evaluate the performance of our IMM-EKF algorithm are the Cramér Rao Lower Bound (CRLB) and the Posterior Cramér Rao Lower Bound (PCRLB) providing a guidance in the improvement of the experimental design. The CRLB is used to assess the estimation of model parameters and the PCRLB for tracking solution. This, combined with a path-loss exponent estimation, the channel calibration algorithm is validated with an online range estimation. The central theme throughout this thesis is to develop a completely online two-slope channel calibration and, simultaneously, a mobile target tracking algorithm. The performance of the method is assessed through realistic computer simulations and validated with real RSS measurements obtained from experimental tests in a typical office environment.Las tecnologías en navegación y localización han estado obteniendo un gran interés en los últimos años donde el Sistema Global de Navegación por Satélite (GNSS) aparece como el más utilizado para estos fines. No obstante, una de las limitaciones del GNSS es la necesidad de tener una visión directa al cielo para así garantizar un posicionamiento bastante fiable. También, al utilizar solamente tecnología GNSS en espacios interiores (más conocidos en el mundo científico por entornos indoor), se es complicado conseguir un buen desempeño en términos de posicionamiento debido a la atenuación e interferencia de la señal causada por los edificios y materiales de construcción. Por esta razón, y debido al crecimiento en aplicaciones dentro de entornos indoor, la investigación de nuevas tecnologías para posicionamiento en interiores se ha centrado en intentar mitigar el mal desempeño de la tecnología GNSS en este tipo de ambientes. En el contexto de posicionamiento en interiores (indoor positioning), se han propuesto multitud de tecnologías emergentes para localización basadas en ultrasonido, infrarrojo, Banda Ultra Ancha (UWB), Zigbee,navegación inercial y otras tecnologías que no sean GNSS. Sin embargo, se requiere de equipo especial y un gran número de fuentes de señal. A pesar de ello, la tecnología en Redes de Área Local Inalámbricas (WLAN) es ampliamente utilizada en el posicionamiento en interiores. Aunque la tecnología WLAN tenga los mismos requerimentos que el resto de tecnologías, en términos de coste y practicidad, los sistemas de posicionamiento basados en redes inalámbricas se utilizan con mayor frecuencia debido al ya existente despliegue de estaciones base (AP) en áreas urbanas e interiores. Existen varias técnicas que sirven para fines de posicionamiento en interiores. Por ejemplo, utilizando el tiempo de llegada de la señal (TOA), las mediciones de la potencia de la señal recibida ( RSS), el ángulo de llegada (AoA), la técnica fingerprinting entre otras. Esta tesis está centrada en sistemas de posicionamiento basados en mediciones WLAN-RSS. Un modelo de canal de atenuación de interiores contruye un mapa de cobertura y también es capaz de reportar los cambios en el entorno indoor. El posicionamiento indoor basado en mediciones RSS se ha convertido en una solución bastante popular, pero las técnicas comunes consideran un modelo de pérdidas por trayectoria de una pendiente, invariante en el tiempo y con un conocimiento previo de los parámetros del canal que se consideran constantes. Esta tesis considera el modelo de pérdidas por trayectoria de pendiente dual y propone una solución robusta para posicionamiento en interiores basado en una arquitectura paralela conformada por un conjunto de algoritmos de Interacción de Múltiples Modelos (IMM) donde cada IMM involucra dos Filtros de Kalman Extendidos (EKF) para el proceso de estimación de la distancia entre el AP y el usuario. Dentro de cada IMM, los parametros del modelo de pérdidas por trayectoria de pendiente dual se estiman secuencialmente utilizando la estimación por máxima verosimilitud (MLE) y así proveer una solución robusta. Finalmente, el conjunto de distancias estimadas se fusionan en un EKF para tener una solución final de la posición del usuario. Además, las cotas de referencias que son derivadas en esta tesis y que sirven para evaluar el rendimiento del algoritmo IMM-EKF son la Cota Inferior de Cramér Rao (CRLB) y la Cota Inferior de Cramér Rao Posterior (PCRLB) que servirán de guía para el perfeccionamiento del diseño experimental. El tema central de esta tesis es desarrollar un algoritmo online para posicionamiento indoor que simultáneamente sea capaz de hacer la calibración del canal de propagación. El desempeño del método se evalúa mediante simulaciones por computadora que se validan con mediciones RSS reales obtenidas a partir de pruebas experimentales.Postprint (published version

Receptor GPS utilizando Dispositivos FPGA

En este trabajo se presenta la implementaci´on de un receptor GNSS que procesa los diferentes datos de navegaci´on contenidos en la se˜nal GPS. Este prototipo se basa en el uso de una plataforma FPGA integrando los diferentes m´odulos de un receptor de se˜nales GPS basado en una arquitectura de Radio Definido por Software. La implementaci´on de los algoritmos sobre dispositivos l´ogicos programables FPGA es para poder tener un receptor con caracter´ısticas programables el cual permita simular otros sistemas de navegaci´on sin necesidad de cambiar dispositivos de hardware sino con solo actualizar algunos par´ametros de configuraci´on dentro de la aplicaci´on de software desarrollada

Cramér-Rao lower bound for breakpoint distance estimation in a path-loss model

This paper addresses the problem of determining the Cramér-Rao lower bound (CRLB) for the parameters and breakpoint distance in a Path-Loss Channel model for Received Signal Strength (RSS) measurements. The path loss model is usually assumed for corrupted RSS measurements due to the shadow fading channel feature. In this paper the two-slope path loss model is considered, in which RSS measurements are modeled differently for close and far distances. Closed-form expressions for the CRLB parameters are derived for unknown breakpoint distance. For unknown parameters and breakpoint distance value, a Bayesian estimation method is proposed. The CRLB is then compared with the performance of the herein proposed method. The comparison illustrates convergence and efficiency of the Bayesian estimator.Postprint (published version

Cramér-Rao lower bound for breakpoint distance estimation in a path-loss model

This paper addresses the problem of determining the Cramér-Rao lower bound (CRLB) for the parameters and breakpoint distance in a Path-Loss Channel model for Received Signal Strength (RSS) measurements. The path loss model is usually assumed for corrupted RSS measurements due to the shadow fading channel feature. In this paper the two-slope path loss model is considered, in which RSS measurements are modeled differently for close and far distances. Closed-form expressions for the CRLB parameters are derived for unknown breakpoint distance. For unknown parameters and breakpoint distance value, a Bayesian estimation method is proposed. The CRLB is then compared with the performance of the herein proposed method. The comparison illustrates convergence and efficiency of the Bayesian estimator

Simultaneous tracking and RSS model calibration by robust filtering

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation’s variance. The performance of the method is shown through numerical simulations in realistic scenarios.Peer ReviewedPostprint (published version

Simultaneous tracking and RSS model calibration by robust filtering

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation’s variance. The performance of the method is shown through numerical simulations in realistic scenarios.Peer Reviewe

Precision-Aided Partial Ambiguity Resolution Technique for Short to Medium Baseline Positioning

GNSS carrier phase observations are fundamental for safety-critical applications where the requirements of accuracy and availability are stringent. Unlike code observations, carrier phase measurements pose high precision at the cost of being ambiguous, since only their fractional part is measured by the receiver. The unknown number of integer cycles, the so-called ambiguities, is to be determined jointly to the dynamical parameters of the target and leads to the mixed real and integer parameter estimation problem. Although the mixed-integer estimation problem has been widely studied, its performance is subject to certain unresolved challenges. For instance, upon the deployment of new GNSS constellation and frequencies, the large number of observations can lead to a decreased probability of successfully mapping the real-valued carrier phase ambiguities to integer ones. In this regard, Partial Ambiguity Resolution (PAR) relaxes the condition of fixing the complete set of ambiguities and finds instead a subset of these based on the optimization of certain objective function (i.e., maximizing the probability of ambiguity resolution success rate). Existing PAR approaches prioritize the chance of finding an integer solution, regardless of whether the posterior positioning result presented a poor accuracy. Such phenomena occur when, for instance, solely a few ambiguities are fixed. To amend this common PAR-related issue, this work introduces the precision-aided PAR, for which the projection of the estimated ambiguities into the position domain is exploited to guide the PAR ambiguity selection process. The premise is straightforward: the covariance matrix for the (fixed) position solution is used to pose a constraint on the integer resolution minimization problem, based on a goal accuracy to achieve. Precision-guided PAR allows to efficiently working with large number of subsets, which makes the algorithm attractive for multi-GNSS multi-frequency applications with stringent availability and accuracy requirements. We leverage on the recently proposed Cramér Rao Bound (CRB) for the mixed-integer model to characterize what is the minimal achievable performance for a subset of observations. The experimentation comprises a synthetic scenario where the performance comparison for Full Ambiguity Resolution (FAR), classical PAR based on sequential observation elimination and the proposed precision-aided PAR is addressed. The evaluation employs multi-GNSS (GPS, Galileo and BeiDou) triple frequency observations and a variety of possible lengths