Results 31 to 40 of about 70,228 (184)
Particle filtering for Quantized Innovations [PDF]
, 2009 In this paper, we re-examine the recently proposed distributed state estimators based on quantized innovations. It is widely believed that the error covariance of the Quantized Innovation Kalman filter follows a modified Riccati recursion.
Hassibi, Babak, Sukhavasi, Ravi Teja
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Distributed finite element Kalman filter [PDF]
2015 European Control Conference (ECC), 2015 This paper addresses state estimation for spatially distributed systems governed by linear partial differential equations from discrete in-space-and-time noisy measurements provided by sensors deployed over the spatial domain of interest. A decentralised and scalable approach is undertaken by decomposing the domain into overlapping subdomains assigned ...
BATTISTELLI, GIORGIO +4 more
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A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Sensors, 2016 The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of ...
Chen Jiang, Shu-Bi Zhang, Qiu-Zhao Zhang
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IEEE Access, 2020 Many positioning and tracking applications use spatially distributed sensor stations, each equipped with coherent measurement channels. The coherent data set of each node is incoherently measured to the data sets of all other nodes, avoiding expensive ...
Markus Hehn, Erik Sippel, Martin Vossiek
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Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs [PDF]
MATEC Web of Conferences, 2019 Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data.
Vazquez-Olguin Miguel +2 more
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Distributed estimation through randomized gossip Kalman filter [PDF]
Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009 In this paper we consider the problem of estimating a random process from noisy measurements, collected by a sensor network. We analyze a distributed two-stage algorithm. The first stage is a Kalman-like estimate update, in which each agent makes use only of its own measurements.
DEL FAVERO, SIMONE, ZAMPIERI, SANDRO
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Kalman Filtering with Uncertain Process and Measurement Noise Covariances with Application to State Estimation in Sensor Networks [PDF]
, 2007 Distributed state estimation under uncertain process and measurement noise covariances is considered. An algorithm based on sensor fusion using Kalman filtering is investigated.
Johansson, Karl Henrik +2 more
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Distributed Kalman filter via Gaussian Belief Propagation [PDF]
2008 46th Annual Allerton Conference on Communication, Control, and Computing, 2008 8 pages, 3 figures, appeared in the 46th Annual Allerton Conference on Communication, Control and Computing, Allerton House, Illinois, Sept ...
Bickson, Danny +2 more
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Vehicle infrastructure cooperative localization using Factor Graphs [PDF]
, 2016 Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure ...
Clarke, David +3 more
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Convergence Analysis of Ensemble Kalman Inversion: The Linear, Noisy Case [PDF]
, 2017 We present an analysis of ensemble Kalman inversion, based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows us to establish well-posedness and convergence results for a fixed ensemble size.
Schillings, Claudia, Stuart, Andrew
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