64,474 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
KD-EKF: A Consistent Cooperative Localization Estimator Based on Kalman Decomposition
In this paper, we revisit the inconsistency problem of EKF-based cooperative
localization (CL) from the perspective of system decomposition. By transforming
the linearized system used by the standard EKF into its Kalman observable
canonical form, the observable and unobservable components of the system are
separated. Consequently, the factors causing the dimension reduction of the
unobservable subspace are explicitly isolated in the state propagation and
measurement Jacobians of the Kalman observable canonical form. Motivated by
these insights, we propose a new CL algorithm called KD-EKF which aims to
enhance consistency. The key idea behind the KD-EKF algorithm involves perform
state estimation in the transformed coordinates so as to eliminate the
influencing factors of observability in the Kalman observable canonical form.
As a result, the KD-EKF algorithm ensures correct observability properties and
consistency. We extensively verify the effectiveness of the KD-EKF algorithm
through both Monte Carlo simulations and real-world experiments. The results
demonstrate that the KD-EKF outperforms state-of-the-art algorithms in terms of
accuracy and consistency
Comparative evaluation of various GPS-free localization algorithm for wireless sensor networks
Wireless Sensor Networks (WSN) are tremendously being used in different environments to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. For example wireless sensors nodes can be designed to detect the ground vibrations generated by silent footsteps of a burglar and trigger an alarm. In many difficult and complex tasks, node localization is very important and critical step to fulfill the purpose of WSN. This project was conducted on the basis of localization of sensor nodes in the scope of GPS-free localizations schemes. We firstly investigated the current localization techniques in wireless scenario for the aim of designing a GPS-free localization scheme based on the local coordinate system formation. A multidimensional scaling method based on dynamic curvilinear belt structure and cooperative localization method was used in this project. Then a simulation result and comparison were carried in MATLAB. The vast majority of current materials on spot discovery in WSNs reflect some beacon nodes with known place. Their spots are then used to look for the positions connected with other normal sensor nodes. Manual rating and configuration means of obtaining spot don't scale and are also error-prone, and equipping sensors with GPS is normally expensive and rule isn't followed in indoor and urban environment. As such, sensor sites can therefore gain from a selfsetting up method where nodes cooperate with each other, estimate nearby distances on their neighbors, and converge to some consistent organize system containing only translation freedom. Dis
Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
We consider cooperative localization technique for mobile agents with
communication and computation capabilities. We start by provide and overview of
different decentralization strategies in the literature, with special focus on
how these algorithms maintain an account of intrinsic correlations between
state estimate of team members. Then, we present a novel decentralized
cooperative localization algorithm that is a decentralized implementation of a
centralized Extended Kalman Filter for cooperative localization. In this
algorithm, instead of propagating cross-covariance terms, each agent propagates
new intermediate local variables that can be used in an update stage to create
the required propagated cross-covariance terms. Whenever there is a relative
measurement in the network, the algorithm declares the agent making this
measurement as the interim master. By acquiring information from the interim
landmark, the agent the relative measurement is taken from, the interim master
can calculate and broadcast a set of intermediate variables which each robot
can then use to update its estimates to match that of a centralized Extended
Kalman Filter for cooperative localization. Once an update is done, no further
communication is needed until the next relative measurement
Cooperative Localization under Limited Connectivity
We report two decentralized multi-agent cooperative localization algorithms
in which, to reduce the communication cost, inter-agent state estimate
correlations are not maintained but accounted for implicitly. In our first
algorithm, to guarantee filter consistency, we account for unknown inter-agent
correlations via an upper bound on the joint covariance matrix of the agents.
In the second method, we use an optimization framework to estimate the unknown
inter-agent cross-covariance matrix. In our algorithms, each agent localizes
itself in a global coordinate frame using a local filter driven by local dead
reckoning and occasional absolute measurement updates, and opportunistically
corrects its pose estimate whenever it can obtain relative measurements with
respect to other mobile agents. To process any relative measurement, only the
agent taken the measurement and the agent the measurement is taken from need to
communicate with each other. Consequently, our algorithms are decentralized
algorithms that do not impose restrictive network-wide connectivity condition.
Moreover, we make no assumptions about the type of agents or relative
measurements. We demonstrate our algorithms in simulation and a
robotic~experiment.Comment: 9 pages, 5 figure
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