Results 11 to 20 of about 94,605 (282)
Physica D: Nonlinear Phenomena, 2001 41 pages, 9 figures, correction of errors in the general multivariate ...
Sornette, Didier, Ide, Kayo
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A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors.
S. Ji, S. Ji, X. Yuan
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Journal of Research of the National Bureau of Standards, 1985 The increased power of small computers makes the use of parameter estimation methods attractive. Such methods have a number of uses in analytical chemistry. When valid models are available, many methods work well, but when models used in the estimation are in error, most methods fail.
Brown, Steven D., Rutan, Sarah C.
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Systems Science & Control Engineering, 2020 In this paper, some recent results on the distributed filtering, estimation and fusion algorithms for nonlinear systems with communication constraints are reviewed.
Zhibin Hu, Jun Hu, Guang Yang
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Robust Kalman Filtering Based on Chi-square Increment and Its Application
Remote Sensing, 2020 In Global Navigation Satellite System (GNSS) positioning, gross errors seriously limit the validity of Kalman filtering and make the final positioning solutions untrustworthy.
Bo Li +7 more
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An Indoor Positioning Method Based on UWB and Visual Fusion
Sensors, 2022 Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas.
Pingping Peng +5 more
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Journal of the Royal Statistical Society Series B: Statistical Methodology, 2000 Summary In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which
Chen, Rong, Liu, Jun S.
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Sensors, 2008 This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering.
Donghua Wang +5 more
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An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship’s SINS under Mooring Conditions
Sensors, 2021 To solve the problem that the ship’s strapdown inertial navigation system (SINS) alignment accuracy decreases with the increase of the nonlinear filtering state dimension under mooring conditions, a method based on Kalman filter (KF) and Adaptive scale ...
Pengchao Yao, Gongliu Yang, Xiafu Peng
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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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