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A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters
2001
Neural Networks
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The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. ...
Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ...
This is a slight improvement to the score of 0.70 obtained with fuzzy ARTMAP with MT-/FD/LUC. ...
doi:10.1016/s0893-6080(01)00019-3
pmid:11341569
fatcat:tfnjmve2izduji3jebetf6kgyu
Analysis of measured radar data for Specific Emitter Identification
2010
2010 IEEE Radar Conference
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A Fuzzy ARTMAP classifier completes the process of specific emitter identification to demonstrate the improvement in Correct Classification Decisions (CCD) using the reduced feature vector. I. ...
The processes developed are pulse extraction, feature calculation, dimensionality reduction and classification. A pulse is detected whenever the phase changes from being random to being linear. ...
Fuzzy ARTMAP is a cognitive neural method combining fuzzy logic and Adaptive Resonance Theory (ART) to create categories of class prototypes to be classified. ...
doi:10.1109/radar.2010.5494658
fatcat:6q5h6pkmi5f33neubhrrjwmkt4
ART Neural Networks for Medical Data Analysis and Fast Distributed Learning
[chapter]
2000
Artificial Neural Networks in Medicine and Biology
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Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ...
Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. ...
Acknowledgements: This research was supported in part by the Office of Naval Research (ONR N00014-95-1-0409 and ONR N00014-95-1-0657). ...
doi:10.1007/978-1-4471-0513-8_2
dblp:conf/annimab/CarpenterM00
fatcat:2mx25gfjcve63kgealmhwtl6ha
MATCH TRACKING STRATEGIES FOR FUZZY ARTMAP NEURAL NETWORKS
[chapter]
2009
Handbook of Pattern Recognition and Computer Vision
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Training fuzzy ARTMAP neural networks for classification using data from complex real-world environments may lead to category proliferation, and yield poor performance. ...
Through a comprehensive set of simulations, it has been observed that by training with MT-, fuzzy ARTMAP expends fewer resources than with other MT strategies, but can achieve a significantly higher generalization ...
Acknowledgements This research was supported in part by the Natural Sciences and Engineering Research Council of Canada, and le Fonds québécois de la recherche sur la nature et les technologies. ...
doi:10.1142/9789814273398_0004
fatcat:lr6gvxy6mreu5ow6k5grvcrwjq
Advances in Multi-Sensor Data Fusion: Algorithms and Applications
2009
Sensors
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Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. ...
Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme. ...
Acknowledgements This research was supported and funded by the State Key Laboratory of Resources and Environment Information System, and Ministry of Science and Technology of China (grant 2006CB403400- ...
doi:10.3390/s91007771
pmid:22408479
pmcid:PMC3292082
fatcat:ucoiobpr55bw7d3qnub2cnd6sa
Convolutional neural network applied to specific emitter identification based on pulse waveform images
2020
IET radar, sonar & navigation
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In the method, a more accurate signal model is built with considering the rising, steady and falling part of the whole PW based on actual radar pulse signals. ...
Simulations and experiments indicate that the proposed method outperforms the existed methods on identifying radar individuals with uncertain modulations and multiple pulse widths in the intercepted pulse ...
Fig. 1 1 Radar emitter pulse signals in the SEI analysis (a) Simulation radar pulse signal with PW of 10 μs, (b) Real radar pulse signal with PW of 1 μs
Fig. 2 2 Graphical PW analysis module in SEI Fig ...
doi:10.1049/iet-rsn.2019.0456
fatcat:lczaxk3zi5b2vnmfonlx2ki6yy
Electronic Warfare:Issues and Challenges for Emitter Classification
2011
Defence Science Journal
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multiple pulse repetition frequencies (PRFs) are highlighted. ...
The proposed approach can play a solution for wide variety of problems in emitter classification in electronic warfare studies. ...
Authors are also thankful to anonymous reviewers for their valuable comments for improving the manuscript. ...
doi:10.14429/dsj.61.529
fatcat:4vf3bi2dzfbcjotzw6mppw5tgq
Survey of Multispectral Image Fusion Techniques in Remote Sensing Applications
[chapter]
2013
New Advances in Image Fusion
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The fuzzy adaptive Kalman filtering algorithm with maneuvering detection was used for largescale maneuvering target which extracts feature data from Kalman filtering processes to estimate the magnitude ...
As discussed in the work of Wang et al., a multi-sensor decision level image fusion algorithm based on fuzzy theory are used for classification of each sensor image, and the classification results are ...
doi:10.5772/56946
fatcat:emb7nxyfcjcgtdq26lvejvegyi
Survey of Multispectral Image Fusion Techniques in Remote Sensing Applications
[chapter]
2011
Image Fusion and Its Applications
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The fuzzy adaptive Kalman filtering algorithm with maneuvering detection was used for largescale maneuvering target which extracts feature data from Kalman filtering processes to estimate the magnitude ...
As discussed in the work of Wang et al., a multi-sensor decision level image fusion algorithm based on fuzzy theory are used for classification of each sensor image, and the classification results are ...
With an emphasis on both the basic and advanced applications of image fusion, this 12-chapter book covers a number of unique concepts that have been graphically represented throughout to enhance readability ...
doi:10.5772/10548
fatcat:t3acfvec5bfgjpjt7hm4lw4iyi
Classifiers Accuracy Improvement Based on Missing Data Imputation
2018
Journal of Artificial Intelligence and Soft Computing Research
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radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. ...
We apply these methods to data samples with up to 60% missingness, this way doubling the number of instances with complete values in the resulting dataset. ...
"What-and-Where fusion strategy" and then apply fuzzy ARTMAP neural network to classify streams of pulses according to radar type, using their functional parameters. ...
doi:10.1515/jaiscr-2018-0002
fatcat:diuvzv53unatnjjvpubptnfbnq
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
[article]
2020
arXiv
pre-print
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Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. ...
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. ...
Parameter clustering technology was proposed to deinterleave the receptive radar pulses based on Hopfield-Kamgar [162] and Fuzzy ART neural network [163] . ...
arXiv:2009.13702v1
fatcat:m6am73324zdwba736sn3vmph3i
Sensors and Technologies in Spain: State-of-the-Art
2014
Sensors
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some improvements from the point of view of economic aspects, including competitiveness and productivity, at the same time different issues of the human life are improved. ...
After the call for papers we received over a hundred of manuscripts, 88 of high quality, of which, were finally selected after a peer review process conducted by prestigious scientists with a high level ...
This device is connected to a microcontroller where data patterns are conveniently processed based on a simplified fuzzy ARTMAP neural network. ...
doi:10.3390/s140815282
pmid:25195853
pmcid:PMC4179053
fatcat:m3oivwfii5f27fltyxwnh76joi
Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification
[chapter]
Studies in Computational Intelligence
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Since its inception in 1992, the fuzzy ARTMAP (FAM) neural network (NN) has attracted researchers' attention as a fast, accurate, off and online pattern classifier. ...
with data complexity). ...
This work was supported in part by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel. ...
doi:10.1007/978-3-540-79474-5_4
fatcat:rg6m43u2fbghdfxnvwx3lfx4lu
Noise Benefits in Expectation-Maximization Algorithms
[article]
2014
arXiv
pre-print
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The convergence speed-up is an example of a noise benefit or "stochastic resonance" in statistical signal processing. ...
This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. ...
Extensions to the ART framework include ARTMAP [44] for supervised classification learning and Fuzzy ART for fuzzy clustering [45] . ...
arXiv:1411.6622v1
fatcat:wanw4zq26fhl5jkrhy3ce2wzbq
Recent advances in mechatronics
1996
Robotics and Autonomous Systems
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The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or ...
Typesetting: Camera-ready by Contributors SPIN 10717447 543210 (Robotics Society of Japan) and SICE (Society of Instr. and Control Engineers of Japan). ...
Tonegawa for his help with the experiments. ...
doi:10.1016/s0921-8890(96)00039-5
fatcat:l5fd4hwa2rbu3l6f2jucyj2fxy
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