We applied the three layer feedforward neural network using the backpropagation learning algorithm. Wedefined classes of radar targets and designated each of them by itsnumber. Our classification process resulted in the number of a radartarget class, which the radar target belongs to....
Radar classification using a neural network Commonly used signal recognition techniques have many drawbacks. Many signal recognition and analysis techniques rely on complex algorithms which are compu... GB Willson - 《Proceedings of Spie Optical Engineering & Photonics in Aerospace Sensing Application of ...
This paper presents a Radar Emitter Identification and Classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitter's parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data ...
基于机器学习的雷达辐射源分类识别技术分析-analysis of radar emitter classification and identification technology based on machine learning.docx,哈尔滨工业大学工学博士学位论文 Abstract 特点与发展历程。着重研究了 RBF 神经网络的网络结构与学习方法。提出了 一
and this irreversible loss of information will cause the loss of the internal data structure of the feature matrix during the training process of the neural network. Usually only the probability of each class needs to be predicted in the classification model, so there is no need to consider the...
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, wavele...
This lays a solid foundation for downstream denoising and classification tasks, while also providing a new direction for radar work mode recognition research based on deep learning. A residual shrinkage module with one-dimensional convolution is integrated into the temporal classification network, ...
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 ...
noise radar, skywave and surface wave over-the-horizon radars, netted LPI sensors, Choi-Williams time-frequency analysis techniques for ftie analysis of LPI waveforms, antiradiation missiles and new seeker designs for detecting LPI emitters and autonomous feature extraction and classification algorithms...
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...