Cooperative Positioning using Massive Differentiation of GNSS Pseudorange Measurements

With Differential GNSS (DGNSS), Single Differentiation (SD) of GNSS pseudorange mea- surements is computed with the aim of correcting harmful errors such as ionospheric and tropospheric delays. These errors can be mitigated to up to very few centimeters, which denotes a performance improvement with respect to the Standard Point Positioning (SPP) solution, widely used in GNSS receivers. However, with DGNSS it is necessary to have a very precise knowledge of the coordinates of a reference station in order to experience this performance improvement. We propose the Massive User-Centric Single Differentiation (MUCSD) algorithm, which is proven to have a comparable performance to DGNSS with- out the need of a reference station. Instead, N cooperative receivers which provide noisy observations of their position and clock bias are introduced in the model. The MUCSD algorithm is mathematically derived with an Iterative Weighted Least Squares (WLS) Estimator. The estimator lower bound is calculated with the Cramér-Rao Bound (CRB). Several scenarios are simulated to test the MUCSD algorithm with the MassiveCoop-Sim simulator. Results show that if the observations provided by the cooperative users have a noise of up to 10 meters, DGNSS performance can be obtained with N = 10. When observations are very noisy, the MUCSD performance still approaches DGNSS for high values of N

Analysis and design of local positioning systems for localizing drones over bounded regions

[ES] El uso de los UAV (Unmanned-Aerial-Vehicles) ha crecido significativamente en los últimos años. Su inserción en el sector civil abre paso a su implementación en el sector agrícola, industrial, así como su uso para aplicaciones de vigilancia o reparto. No obstante, el desarrollo eficiente de estas aplicaciones depende de la capacidad del dron de posicionarse de forma autónoma. Si bien es común encontrar drones con sistemas de posicionamiento satelital (GNSS), estos sistemas resultan insuficientes para la navegación autónoma en entornos urbanos o de interiores. En estos escenarios, la implementación de sistemas de posicionamiento local (LPS) resulta de gran interés por su capacidad de adaptación. A través de la distribución óptima de las balizas que constituyen este sistema pueden adaptarse a la mayoría de entornos, así como mejorar sus prestaciones. No obstante, la complejidad de este problema se ha caracterizado como NP-Hard, lo que dificulta su resolución. En este Trabajo de Fin de Máster se desarrolla un algoritmo genético para optimizar LPS en diferentes entornos. Este algoritmo, innovador en el diseño de LPS para UAV, se prueba sobre un entorno urbano diseñado. Los resultados obtenidos denotan la validez de la metodología al obtener incertidumbres en la localización significativamente menores que los GNSS, siendo además substancial la mejoría introducida por el algoritmo genético diseñado.[EN] Unmanned-Aerial-Vehicles (UAV) widespread use have grown significantly in recent years. Their insertion in the civil sector allows their implementation in the agricultural and industrial sectors, as well as their use for surveillance or delivery applications. However, the efficient development of these applications depends on the drone’s ability to position itself autonomously. Although it is common to find drones with satellite positioning systems (GNSS), these systems are insufficient for autonomous navigation in urban or indoor environments. In these scenarios, the implementation of local positioning systems (LPS) is widely spread due to their adaptability capabilities. Through the optimal distribution of the sensors that constitute this system, they can adapt to almost any environment while also improving its performance. However, the complexity of this problem has been characterized as NP-Hard, which complicates its resolution. In this Master’s Final Project, a genetic algorithm is developed to optimize LPS in different environments. This algorithm, pioneer in the design of LPS for UAV localization, is tested on a generated urban environment. The results obtained denote the effectiveness of the methodology by obtaining location uncertainties significantly lower than GNSS. Moreover, the proposed genetic algorithm achieves greater results than non-optimized distributions, proving its capabilities

Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception