5 research outputs found

    BaSIS-Net: From point estimate to predictive distribution in neural networks - a Bayesian sequential importance sampling framework

    Get PDF
    Data-driven Deep Learning (DL) models have revolutionized autonomous systems, but ensuring their safety and reliability necessitates the assessment of predictive confidence or uncertainty. Bayesian DL provides a principled approach to quantify uncertainty via probability density functions defined over model parameters. However, the exact solution is intractable for most DL models, and the approximation methods, often based on heuristics, suffer from scalability issues and stringent distribution assumptions and may lack theoretical guarantees. This work develops a Sequential Importance Sampling framework that approximates the posterior probability density function through weighted samples (or particles), which can be used to find the mean, variance, or higher-order moments of the posterior distribution. We demonstrate that propagating particles, which capture information about the higher-order moments, through the layers of the DL model results in increased robustness to natural and malicious noise (adversarial attacks). The variance computed from these particles effectively quantifies the model’s decision uncertainty, demonstrating well-calibrated and accurate predictive confidence

    Wireless Localization Systems: Statistical Modeling and Algorithm Design

    Get PDF
    Wireless localization systems are essential for emerging applications that rely on context-awareness, especially in civil, logistic, and security sectors. Accurate localization in indoor environments is still a challenge and triggers a fervent research activity worldwide. The performance of such systems relies on the quality of range measurements gathered by processing wireless signals within the sensors composing the localization system. Such range estimates serve as observations for the target position inference. The quality of range estimates depends on the network intrinsic properties and signal processing techniques. Therefore, the system design and analysis call for the statistical modeling of range information and the algorithm design for ranging, localization and tracking. The main objectives of this thesis are: (i) the derivation of statistical models and (ii) the design of algorithms for different wire- less localization systems, with particular regard to passive and semi-passive systems (i.e., active radar systems, passive radar systems, and radio frequency identification systems). Statistical models for the range information are derived, low-complexity algorithms with soft-decision and hard-decision are proposed, and several wideband localization systems have been analyzed. The research activity has been conducted also within the framework of different projects in collaboration with companies and other universities, and within a one-year-long research period at Massachusetts Institute of Technology, Cambridge, MA, USA. The analysis of system performance, the derived models, and the proposed algorithms are validated considering different case studies in realistic scenarios and also using the results obtained under the aforementioned projects

    Particle filters for tracking in wireless sensor networks

    Get PDF
    The goal of this thesis is the development, implementation and assessment of efficient particle filters (PFs) for various target tracking applications on wireless sensor networks (WSNs). We first focus on developing efficient models and particle filters for indoor tracking using received signal strength (RSS) in WSNs. RSS is a very appealing type of measurement for indoor tracking because of its availability on many existing communication networks. In particular, most current wireless communication networks (WiFi, ZigBee or even cellular networks) provide radio signal strength (RSS) measurements for each radio transmission. Unfortunately, RSS in indoor scenarios is highly influenced by multipath propagation and, thus, it turns out very hard to adequately model the correspondence between the received power and the transmitterto- receiver distance. Further, the trajectories that the targets perform in indoor scenarios usually have abrupt changes that result from avoiding walls and furniture and consequently the target dynamics is also difficult to model. In Chapter 3 we propose a flexible probabilistic scheme that allows the description of different classes of target dynamics and propagation environments through the use of multiple switching models. The resulting state-space structure is termed a generalized switching multiple model (GSMM) system. The drawback of the GSMM system is the increase in the dimension of the system state and, hence, the number of variables that the tracking algorithm has to estimate. In order to handle the added difficulty, we propose two Rao-Blackwellized particle filtering (RBPF) algorithms in which a subset of the state variables is integrated out to improve the tracking accuracy. As the main drawback of the particle filters is their computational complexity we then move on to investigate how to reduce it via de distribution of the processing. Distributed applications of tracking are particularly interesting in situations where high-power centralized hardware cannot be used. For example, in deployments where computational infrastructure and power are not available or where there is no time or trivial way of connecting to it. The large majority of existing contributions related to particle filtering, however, only offer a theoretical perspective or computer simulation studies, owing in part to the complications of real-world deployment and testing on low-power hardware. In Chapter 4 we investigate the use of the distributed resampling with non-proportional allocation (DRNA) algorithm in order to obtain a distributed particle filtering (DPF) algorithm. The DRNA algorithm was devised to speed up the computations in particle filtering via the parallelization of the resampling step. The basic assumption is the availability of a set of processors interconnected by a high-speed network, in the manner of state-of-the-art graphical processing unit (GPU) based systems. In a typical WSN, the communications among nodes are subject to various constraints (i.e., transmission capacity, power consumption or error rates), hence the hardware setup is fundamentally different. We first revisit the standard PF and its combination with the DRNA algorithm, providing a formal description of the methodology. This includes a simple analysis showing that (a) the importance weights are proper and (b) the resampling scheme is unbiased. Then we address the practical implementation of a distributed PF for target tracking, based on the DRNA scheme, that runs in real time over a WSN. For the practical implementation of the methodology on a real-time WSN, we have developed a software and hardware testbed with the required algorithmic and communication modules, working on a network of wireless light-intensity sensors. The DPF scheme based on the DRNA algorithm guarantees the computation of proper weights and consistent estimators provided that the whole set of observations is available at every time instant at every node. Unfortunately, due to practical communication constraints, the technique described in Chapter 4 may turn out unrealistic for many WSNs of larger size. We thus investigate in Chapter 5 how to relax the communication requirements of the DPF algorithm using (a) a random model for the spread of data over the WSN and (b) methods that enable the out-of-sequence processing of sensor observations. The presented observation spread scheme is flexible and allows tuning of the observation spread over the network via the selection of a parameter. As the observation spread has a direct connection with the precision on the estimation, we have also introduced a methodology that allows the selection of the parameter a priori without the need of performing any kind of experiment. The performance of the proposed scheme is assessed by way of an extensive simulation study.De forma general, el objetivo de esta tesis doctoral es el desarrollo y la aplicación de filtros de partículas (FP) eficientes para diversas aplicaciones de seguimiento de blancos en redes de sensores inalámbricas (wireless sensor networks o WSNs). Primero nos centramos en el desarrollo de modelos y filtros de partículas para el seguimiento de blancos en entornos de interiores mediante el uso de medidas de potencia de señal de radio (received signal strength o RSS) en WSNs. Las medidas RSS son un tipo de medida muy utilizada debido a su disponibilidad en redes ya implantadas en muchos entornos de interiores. De hecho, en muchas redes de comunicaciones inalámbricas actuales (WiFi, ZigBee o incluso las redes de telefonía móvil), se pueden obtener medidas de RSS sin necesidad de modificación alguna. Desafortunadamente, las medidas RSS en entornos de interiores suelen distorsionarse debido a la propagación multitrayecto por lo que resulta muy difícil modelar adecuadamente la relación entre la potencia de señal recibida y la distancia entre el transmisor y el receptor. Otra dificultad añadida en el seguimiento de interiores es que las trayectorias realizadas por los blancos suelen tener por lo general cambios muy bruscos y en consecuencia el modelado de las trayectorias dinámicas es una actividad muy compleja. En el Capítulo 3 se propone un esquema probabilístico flexible que permite la descripción de los diferentes sistemas dinámicos y entornos de propagación mediante el uso de múltiples modelos conmutables entre sí. Este esquema permite la descripción de varios modelos dinámicos y de propagación de forma muy precisa de manera que el filtro sólo tiene que estimar el modelo adecuado a cada instante para poder hacer el seguimiento. El modelo de estado resultante (modelo de conmutación múltiple generalizado, generalized switiching multiple model o GSMM) tiene el inconveniente del aumento de la dimensión del estado del sistema y, por lo tanto, el número de variables que el algoritmo de seguimiento tiene que estimar. Para superar esta dificultad, se proponen varios algoritmos de filtros de partículas con reducción de la varianza (Rao-Blackwellized particle filtering (RBPF) algorithms) en el que un subconjunto de las variables de estado, incluyendo las variables indicadoras de observación, se integran a fin de mejorar la precisión de seguimiento. Dado que la mayor desventaja de los filtros de partículas es su complejidad computacional, a continuación investigamos cómo reducirla distribuyendo el procesado entre los diferentes nodos de la red. Las aplicaciones distribuidas de seguimiento en redes de sensores son de especial interés en muchas implementaciones reales, por ejemplo: cuando el hardware usado no tiene suficiente capacidad computacional, si se quiere alargar la vida de la red usando menos energía, o cuando no hay tiempo (o medios) para conectarse a la toda la red. La reducción de complejidad también es interesante cuando la red es tan extensa que el uso de hardware con alta capacidad de procesamiento la haría excesivamente costosa. La mayoría de las contribuciones existentes ofrecen exclusivamente una perspectiva teórica o muestran resultados sintéticos o simulados, debido en parte a las complicaciones asociadas a la implementación de los algoritmos y de las pruebas en un hardware con nodos de baja capacidad computacional. En el Capítulo 4 se investiga el uso del algoritmo distributed resampling with non proportional allocation (DRNA) a fin de obtener un filtro de partículas distribuido (FPD) para su implementación en una red de sensores real con nodos de baja capacidad computacional. El algoritmo DRNA fue elaborado para acelerar el cómputo del filtro de partículas centrándose en la paralelización de uno de sus pasos: el remuestreo. Para ello el DRNA asume la disponibilidad de un conjunto de procesadores interconectados por una red de alta velocidad. En una red de sensores inalábmrica, las comunicaciones entre los nodos suelen tener restricciones (debido a la capacidad de transmisión, el consumo de energía o de las tasas de error), y en consecuencia, la configuración de hardware es fundamentalmente diferente. En este trabajo abordamos el problema de la aplicación del algoritmo de DRNA en una WSN real. En primer lugar, revisamos el FP estándar y su combinación con el algoritmo DRNA, proporcionando una descripci´on formal de la metodología. Esto incluye un análisis que demuestra que (a) los pesos se calculan de forma adecuada y (b) que el paso del remuestreo no introduce ningún sesgo. A continuación describimos la aplicación práctica de un FP distribuido para seguimiento de objetivos, basado en el esquema DRNA, que se ejecuta en tiempo real a través de una WSN. Hemos desarrollado un banco de pruebas de software y hardware donde hemos usado unos nodos con sensores que miden intensidad de la luz y que a su vez tienen una capacidad de procesamiento y de comunicaciones limitada. Evaluamos el rendimiento del sistema de seguimiento en términos de error de la trayectoria estimada usando los datos sintéticos y evaluamos la capacidad computacional con datos reales. El filtro de partículas distribuído basado en el algoritmo DRNA garantiza el cómputo correcto de los pesos y los estimadores a condición de que el conjunto completo de observaciones estén disponibles en cada instante de tiempo y en cada nodo. Debido a limitaciones de comunicación esta metodología puede resultar poco realista para su implementación en muchas redes de sensores inalámbricas de tamaño grande. Por ello, en el Capítulo 5 investigamos cómo reducir los requisitos de comunicación del algoritmo anterior mediante (a) el uso de un modelo aleatorio para la difusión de datos de observación a través de las red y (b) la adaptación de los filtros para permitir el procesamiento de observaciones que lleguen fuera de secuencia. El esquema presentado permite reducir la carga de comunicaciones en la red a cambio de una reducción en la precisión del algoritmo mediante la selección de un parámetro de diseño. También presentamos una metodología que permite la selección de dicho parámetro que controla la difusión de las observaciones a priori sin la necesidad de llevar a cabo ningún tipo de experimento. El rendimiento del esquema propuesto ha sido evaluado mediante un estudio extensivo de simulaciones

    Colocated multiple-input multiple-output radars for smart mobility

    Get PDF
    In recent years, radars have been used in many applications such as precision agriculture and advanced driver assistant systems. Optimal techniques for the estimation of the number of targets and of their coordinates require solving multidimensional optimization problems entailing huge computational efforts. This has motivated the development of sub-optimal estimation techniques able to achieve good accuracy at a manageable computational cost. Another technical issue in advanced driver assistant systems is the tracking of multiple targets. Even if various filtering techniques have been developed, new efficient and robust algorithms for target tracking can be devised exploiting a probabilistic approach, based on the use of the factor graph and the sum-product algorithm. The two contributions provided by this dissertation are the investigation of the filtering and smoothing problems from a factor graph perspective and the development of efficient algorithms for two and three-dimensional radar imaging. Concerning the first contribution, a new factor graph for filtering is derived and the sum-product rule is applied to this graphical model; this allows to interpret known algorithms and to develop new filtering techniques. Then, a general method, based on graphical modelling, is proposed to derive filtering algorithms that involve a network of interconnected Bayesian filters. Finally, the proposed graphical approach is exploited to devise a new smoothing algorithm. Numerical results for dynamic systems evidence that our algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other techniques in the literature. Regarding radar imaging, various algorithms are developed for frequency modulated continuous wave radars; these algorithms rely on novel and efficient methods for the detection and estimation of multiple superimposed tones in noise. The accuracy achieved in the presence of multiple closely spaced targets is assessed on the basis of both synthetically generated data and of the measurements acquired through two commercial multiple-input multiple-output radars
    corecore