Pangu-Weather and similar models, such as Nvidia’s FourcastNet and Google-DeepMind’s GraphCast, are making meteorologists “reconsider how we use machine learning and weather forecasts,” says Peter Dueben, head of Earth system modeling at ECMWF. In the past year, multiple tech companies have unveiled AI models that aim to improve weather forecasting. Pangu-Weather is exciting because it can forecast weather much faster than scientists were able to before and forecast things that weren’t in its original training data, says Fuhrer. This finding shows that machine-learning models are able to pick up on the physical processes of weather and generalize them to situations they haven’t seen before, says Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. Pangu-Weather was also able to accurately track the path of a tropical cyclone, despite not having been trained with data on tropical cyclones. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.The researchers tested Pangu-Weather against one of the leading conventional weather prediction systems in the world, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF), and found that it produced similar accuracy. The experiment is repeated for various noise levels up to -12dB SNR.
NEURAL NETWORK RADAR FREE
The success rate achieved is 100 % for noise free signals. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Using this algorithm, the BF images of the signals are obtained. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. The classification approach is based on the following two steps. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. LPI radar, signal recognition, cyclostationary (CS), cyclic autocorrelation function (CACF), spectral correlation density (SCD), Bi-frequency (BF), contour plot, denoising, multilayer perceptron neural (MLPN) network, confusion matrix, Artificial Neural Networks Abstract
Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh 522502, India Department of ECE, VNRVJIET, Hyderabad 500090, Indiaĭepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh 522502, Indiaĭepartment of Electronics and Communication Engineering (Retd.), Osmania University, Hyderabad 500007, India
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