Results 291 to 300 of about 535,764 (342)

Fusion of Algorithms for Compressed Sensing

IEEE Transactions on Signal Processing, 2013
For compressed sensing (CS), we develop a new scheme inspired by data fusion principles. In the proposed fusion based scheme, several CS reconstruction algorithms participate and they are executed in parallel, independently. The final estimate of the underlying sparse signal is derived by fusing the estimates obtained from the participating algorithms.
Sooraj K. Ambat, Saikat Chatterjee, K. V. S. Hari   +2 more
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Multisensory Fusion Algorithms for Tracking

Proceedings. The First IEEE Regional Conference on Aerospace Control Systems,, 1993
In this paper we extend a multitarget tracking algorithm for use in multisensor tracking situations. The algorithm we consider is Joint Probabilistic Data Association (JPDA). JPDA is extended to handle an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. We also show how filtering can be
Sean D. O'Neil, Lucy Y. Pao
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Sensor fusion in estimation algorithms

Journal of the Franklin Institute, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David D. Sworder, John E. Boyd
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Fusion of multiple positioning algorithms

2011 8th International Conference on Information, Communications & Signal Processing, 2011
With the proliferation of location based services (LBS), various indoor positioning techniques have been explored based on received signal strength (RSS). To improve performance, many hybrid or fusion approaches have been proposed in the literature. In this paper, a new fusion approach is proposed to achieve better positioning performance, with a focus
Lei Wang, Wai-Choong Wong
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Genetic algorithms in classifier fusion

Applied Soft Computing, 2006
An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain.
Bogdan Gabrys, Dymitr Ruta
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A Multi-clustering Fusion Algorithm

2002
A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a partition of the data which is not affected by initialization and overcomes the ...
Dimitrios S. Frossyniotis, Minas Pertselakis, Andreas Stafylopatis   +2 more
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The Fusion of SRC and SRRC Algorithms

2014
As a recently proposed technique, sparse representation based classi-fication(SRC) and sparse residue representation classification SRRChave been widely used for face recognition(FR).SRC and SRRC represent the test sample as a linear combination of training samples.
Ke Yan, Jian Cao
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Estimation Fusion Algorithm

2020
Multiple source information-based autonomous navigation is essentially an estimation fusion problem. This chapter will go deep into the estimation fusion algorithm. Section 3.1 presents linear models and algorithms, mainly including linear unified model and its fusion algorithm as well as covariance intersection algorithm in the distributed fusion ...
Dayi Wang, Maodeng Li, Xiangyu Huang, Xiaowen Zhang   +3 more
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DATA FUSION IN SEVERAL ALGORITHMS

Advances in Adaptive Data Analysis, 2013
Data fusion consists of the process of integrating several datasets with some common variables, and other variables available only in partial datasets. The main problem of data fusion can be described as follows. From one source, having X0 and Y0 datasets (with N0 observations by multiple x and y variables, n and m of those, respectively), and from ...
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A Robust Fusion Algorithm for Sensor Failure

IEEE Signal Processing Letters, 2013
Accurate multimodal and multisensor detection of a target phenomenon requires knowledge of probabilistic sensor characteristics to determine an appropriate fusion rule which optimizes an objective of interest, traditionally the expected Bayesian risk. However, a particular sensor characteristic can change online, introducing unaccounted additional risk
Matt Higger, Murat Akçakaya, Deniz Erdogmus   +2 more
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