Distributed multiple model joint probabilistic data association with Gibbs sampling-aided implementation

This paper proposes a new distributed multiple model multiple manoeuvring target tracking algorithm. The proposed tracker is derived by combining joint probabilistic data association (JPDA) with consensus-based distributed filtering. Exact implementation of the JPDA involves enumerating all possible joint association events and thus often becomes computationally intractable in practice. We propose a computationally tractable approximation of calculating the marginal association probabilities for measurement-target mappings based on stochastic Gibbs sampling. In order to achieve scalability for a large number of sensors and high tolerance to sensor failure, a simple average consensus algorithm-based information JPDA filter is proposed for distributed tracking of multiple manoeuvring targets. In the proposed framework, the state of each target is updated using consensus-based information fusion while the manoeuvre mode probability of each target is corrected with measurement probability fusion. Simulations clearly demonstrate the effectiveness and characteristics of the proposed algorithm. The results reveal that the proposed formulation is scalable and much more efficient than classical JPDA without sacrificing tracking accurac

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.Comment: 13 pages, 33 figure

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics

This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2

Survey of location-centric target tracking with mobile elements in wireless sensor networks

介绍目标跟踪的过程以及移动跟踪的特点;通过区分目标定位为主的方法和目标探测为主的方法,介绍定位为主的移动式目标跟踪方法(称为目标的移动式定位跟踪; )的研究现状;分析和比较不同方法的特点和应用领域,发现现有方法虽然可以提高跟踪质量、降低网络整体能耗,但是还存在一些问题。基于此,总结目标的移动; 式定位跟踪方法在方法类型、网络结构和节点模型等方面可能存在的研究热点,指出其研究和发展趋势。The basic process of target tracking and the properties of tracking; solutions with mobile elements were introduced. By distinguishing; location-centric methods and detection-centric methods, the current; research status of the location-centric target tracking methods were; reviewed. The properties and application fields of different solutions; were analyzed and compared. Although the existing solutions can; significantly improve tracking quality and reduce energy consumption of; the whole network, there are also some problems. Based on these; discoveries, some possible research hotspots of mobile solutions were; summarized in many aspects, such as method types, network architecture,; node model, and so on, indicating the future direction of research and; development.国家自然科学基金资助项目; 国家科技支撑计划项

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Particle filters for tracking in wireless sensor networks

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