10 research outputs found

    Markov chain distributed particle filters (mcdpf

    Get PDF
    Abstract-Distributed particle filters (DPF) are known to provide robustness for the state estimation problem and can reduce the amount of information communication compared to centralized approaches. Due to the difficulty of merging multiple distributions represented by particles and associated weights, however, most uses of DPF to date tend to approximate the posterior distribution using a parametric model or to use a predetermined message path. In this paper, the Markov Chain Distributed Particle Filter (MCDPF) algorithm is proposed, based on particles performing random walks across the network. This approach maintains robustness since every sensor only needs to exchange particles and weights locally and furthermore enables more general representations of posterior distributions because there are no a priori assumptions on distribution form. The paper provides a proof of weak convergence of the MCDPF algorithm to the corresponding centralized particle filter and the optimal filtering solution, and concludes with a numerical study showing that MCDPF leads to a reliable estimation of the posterior distribution of a nonlinear system

    Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks

    Full text link
    In distributed target tracking for wireless sensor networks, agreement on the target state can be achieved by the construction and maintenance of a communication path, in order to exchange information regarding local likelihood functions. Such an approach lacks robustness to failures and is not easily applicable to ad-hoc networks. To address this, several methods have been proposed that allow agreement on the global likelihood through fully distributed belief consensus (BC) algorithms, operating on local likelihoods in distributed particle filtering (DPF). However, a unified comparison of the convergence speed and communication cost has not been performed. In this paper, we provide such a comparison and propose a novel BC algorithm based on belief propagation (BP). According to our study, DPF based on metropolis belief consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus is the fastest in tree graphs. Moreover, we found that BC-based DPF methods have lower communication overhead than data flooding when the network is sufficiently sparse

    A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

    Get PDF
    [EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

    Convergence of Distributed Flooding and Its Application for Distributed Bayesian Filtering

    Get PDF
    Distributed flooding is a fundamental information sharing method to get network consensus via peer-to-peer communication. However, a unified consensus-oriented formulation of the algorithm and its convergence performance are not yet explicitly available in the literature. To fill this void in this paper, set-theoretic flooding rules are defined by encapsulating the information of interest in finite sets (one set per node), namely distributed set-theoretic information flooding (DSIF). This leads to a new type of consensus referred to as ”collecting consensus” which aims to ensure that all nodes get the same information. Convergence and optimality analyses are provided based on a consistent measure of the degree of consensus (DoC) of the network. Compared with the prevailing averaging consensus, the proposed DSIF protocol benefits from avoiding repeated use of any information and offering the highest converging efficiency for network consensus while being exposed to increasing node-storage requirements against communication iterations and higher communication load. The protocol has been advocated for distributed nonlinear Bayesian filtering, where each node operates a separate particle filter, and the collecting consensus is pursued on the sensor data alone or jointly with intermediate local estimates. Simulations are provided in detail to demonstrate the theoretical findings

    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

    Distributed implementations of the particle filter with performance bounds

    Get PDF
    The focus of the thesis is on developing distributed estimation algorithms for systems with nonlinear dynamics. Of particular interest are the agent or sensor networks (AN/SN) consisting of a large number of local processing and observation agents/nodes, which can communicate and cooperate with each other to perform a predefined task. Examples of such AN/SNs are distributed camera networks, acoustic sensor networks, networks of unmanned aerial vehicles, social networks, and robotic networks. Signal processing in the AN/SNs is traditionally centralized and developed for systems with linear dynamics. In the centralized architecture, the participating nodes communicate their observations (either directly or indirectly via a multi-hop relay) to a central processing unit, referred to as the fusion centre, which is responsible for performing the predefined task. For centralized systems with linear dynamics, the Kalman filter provides the optimal approach but suffers from several drawbacks, e.g., it is generally unscalable and also susceptible to failure in case the fusion centre breaks down. In general, no analytic solution can be determined for systems with nonlinear dynamics. Consequently, the conventional Kalman filter cannot be used and one has to rely on numerical approaches. In such cases, the sequential Monte Carlo approaches, also known as the particle filters, are widely used as approximates to the Bayesian estimators but mostly in the centralized configuration. Recently there has been a growing interest in distributed signal processing algorithms where: (i) There is no fusion centre; (ii) The local nodes do not have (require) global knowledge of the network topology, and; (iii) Each node exchanges data only within its local neighborhood. Distributed estimation have been widely explored for estimation/tracking problems in linear systems. Distributed particle filter implementations for nonlinear systems are still in their infancy and are the focus of this thesis. In the first part of this thesis, four different consensus-based distributed particle filter implementations are proposed. First, a constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for bearing-only tracking (BOT) and joint bearing/range tracking problems encountered in a number of applications including radar target tracking and robot localization. Although the number of parallel consensus runs in the CSS/DPF is lower compared to the existing distributed implementations of the particle filter, the CSS/DPF still requires a large number of iterations for the consensus runs to converge. To further reduce the consensus overhead, the CSS/DPF is extended to distributed implementation of the unscented particle filter, referred to as the CSS/DUPF, which require a limited number of consensus iterations. Both CSS/DPF and CSS/DUPF are specific to BOT and joint bearing/range tracking problems. Next, the unscented, consensus-based, distributed implementation of the particle filter (UCD /DPF) is proposed which is generalizable to systems with any dynamics. In terms of contributions, the UCD /DPF makes two important improvements to the existing distributed particle filter framework: (i) Unlike existing distributed implementations of the particle filter, the UCD /DPF uses all available global observations including the most recent ones in deriving the proposal distribution based on the distributed UKF, and; (ii) Computation of the global estimates from local estimates during the consensus step is based on an optimal fusion rule. Finally, a multi-rate consensus/fusion based framework for distributed implementation of the particle filter, referred to as the CF /DPF, is proposed. Separate fusion filters are designed to consistently assimilate the local filtering distributions into the global posterior by compensating for the common past information between neighbouring nodes. The CF /DPF offers two distinct advantages over its counterparts. First, the CF /DPF framework is suitable for scenarios where network connectivity is intermittent and consensus can not be reached between two consecutive observations. Second, the CF /DPF is not limited to the Gaussian approximation for the global posterior density. Numerical simulations verify the near-optimal performance of the proposed distributed particle filter implementations. The second half of the thesis focuses on the distributed computation of the posterior Cramer-Rao lower bounds (PCRLB). The current PCRLB approaches assume a centralized or hierarchical architecture. The exact expression for distributed computation of the PCRLB is not yet available and only an approximate expression has recently been derived. Motivated by the distributed adaptive resource management problems with the objective of dynamically activating a time-variant subset of observation nodes to optimize the network's performance, the thesis derives the exact expression, referred to as the dPCRLB, for computing the PCRLB for any AN/SN configured in a distributed fashion. The dPCRLB computational algorithms are derived for both the off-line conventional (non-conditional) PCRLB determined primarily from the state model, observation model, and prior knowledge of the initial state of the system, and the online conditional PCRLB expressed as a function of past history of the observations. Compared to the non-conditional dPCRLB, its conditional counterpart provides a more accurate representation of the estimator's performance and, consequently, a better criteria for sensor selection. The thesis then extends the dPCRLB algorithms to quantized observations. Particle filter realizations are used to compute these bounds numerically and quantify their performance for data fusion problems through Monte-Carlo simulations

    Resilient Infrastructure and Building Security

    Get PDF

    Nonparametric Message Passing Methods for Cooperative Localization and Tracking

    Get PDF
    The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models
    corecore