We found that the multilayer perceptron using error back propagation has the highest performance for the pulse sorting problem and comparable performance to conventional radar classification techniques.Gregory B. WillsonApplications of Artificial Neural Networks, V.21
This example presents a workflow for performing radar target classification using machine and deep learning techniques. Although this example used synthesized data to do training and testing, it can be easily extended to accommodate real radar returns. Because of the signal characteristics, wavelet...
Our results on the mechanism of radar’s double-bounce effect in terms of the backscatter changes between urban water and impervious land cover can provide more insights into urban land use classification using a single SAR image when the relationship between different urban surface morphological ...
This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN). Modulation classification is an important function for an intelligent receiver. Modulation classification has numerous applications, such as ...
In a multifunction radar (MFR), different waveforms are scheduled for surveillance, detection, tracking, or classification. Waveform selection may use NNs or other optimization techniques. Waveform selection can be a single step or multiple steps ahead. Both fixed and variable waveform libraries have...
Convolutional neural networkDeep learningGait energy imageGait recognitionHuman gait identificationMulti-task learningGait as a biometric feature that can be ... Y Chao,B Zhang,F Coenen 被引量: 3发表: 2015年 Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep\n Learning Approaches...
PointNet: deep learning on point sets for 3D classification and segmentation. Water Sci. Technol. 30, 95–104 (2017). Article Google Scholar Badrinarayanan, V., Kendall, A. & Cipolla, R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern ...
In addition, the idea of transfer learning was applied for radar-based HAR to evaluate the classification performance of a pre-trained network. For this, GoogLeNet was taken and trained on the newly-produced hybrid maps. These initial results showed that the LeNet-5 CNN using only the ...
Visualize classification performance using a confusion matrix. Get confusionchart(predictedLabels,actualLabels); The largest confusion is between counterclockwise and clockwise movements and inward push and empty movements. Explore Network Predictions You can obtain the scores from the final max-pooling ...
Use a multiple-input, single-output convolutional neural network (CNN) where the CNN model extracts feature information from each signal before combining it to make a final gesture label prediction. Hand Gesture Classification Using Radar Signals and Deep Learning Sorry, your browser doesn't ...