2,484 research outputs found
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure
Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages
This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times
Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Growing progress in sensor technology has constantly expanded the number and
range of low-cost, small, and portable sensors on the market, increasing the
number and type of physical phenomena that can be measured with wirelessly
connected sensors. Large-scale deployments of wireless sensor networks (WSN)
involving hundreds or thousands of devices and limited budgets often constrain
the choice of sensing hardware, which generally has reduced accuracy,
precision, and reliability. Therefore, it is challenging to achieve good data
quality and maintain error-free measurements during the whole system lifetime.
Self-calibration or recalibration in ad hoc sensor networks to preserve data
quality is essential, yet challenging, for several reasons, such as the
existence of random noise and the absence of suitable general models.
Calibration performed in the field, without accurate and controlled
instrumentation, is said to be in an uncontrolled environment. This paper
provides current and fundamental self-calibration approaches and models for
wireless sensor networks in uncontrolled environments
Distributed data association for multi-target tracking in sensor networks
Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more
challenging in the context of sensor networks, since association is coupled
across the network, yet centralized data processing is in general
infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data
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