scholarly journals Neural network directed Bayes decision rule for moving target classification

2000 ◽  
Vol 36 (1) ◽  
pp. 176-188 ◽  
Author(s):  
Xi Yu ◽  
M.R. Azimi-Sadjadi
Keyword(s):  
Neural Network ◽  
Decision Rule ◽  
Moving Target ◽  
Target Classification

USE OF NEURAL NETS TO MEASURE THE τ POLARIZATION AND ITS BAYESIAN INTERPRETATION

1991 ◽  
Vol 02 (03) ◽  
pp. 221-228 ◽  
Author(s):  
Lluís Garrido ◽  
Vicens Gaitan
Keyword(s):  
Neural Network ◽  
Decision Rule ◽  
Bayesian Method ◽  
Neural Nets ◽  
Bayesian Decision ◽  
Natural Way

We have tested a neural network (NN) technique as a method to determine the helicity of the τ particles in the process: e+e−→(Z0, γ*)→τ+τ−→(ρν)(ρν). It takes into account in a natural way the fact that both taus have different helicity and gives efficiencies comparable to the Bayesian method. We have found this “academic” example a nice way to introduce the analytical interpretation of the net output, showing that these neural nets techniques are equivalent to a Bayesian Decision Rule.

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

2021 ◽  
Author(s):  
M. Bharat Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Fuzzy System ◽  
Deep Convolutional Neural Network ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Deep Cnn

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

SAR moving target imaging based on convolutional neural network

2020 ◽  
Vol 106 ◽  
pp. 102832
Author(s):  
Zhi-jun Lu ◽  
Qi Qin ◽  
Hong-yin Shi ◽  
Hao Huang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Moving Target ◽  
Target Imaging ◽  
Moving Target Imaging
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