1/1/2024 0 Comments Neural network radar![]() ![]() 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 experiment is repeated for various noise levels up to -12dB SNR. The success rate achieved is 100 % for noise free signals. Rainnet v1.0: a convolutional neural network for radar-based precipitation nowcasting. 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. Radar signal categorization using a neural network. 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 The ANNs used were a simple feed-forward network (FF), a recurrent neural network (RNN), and a time-delay neural network (TDNN).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 The mel-cepstrum coefficients were extracted from anger, fear, and pain cries. The input data consists of successive frames of 10 mel-cepstrum coefficients ranging in length from 0.75 seconds to 1 second. ![]() This paper presents the results of another attempt at automating the discrimination process, this time using artificial neural networks (ANNs). To date, research groups have determined that a number of different types of cries can be determined auditorily and at least one group has attempted to automate this classification process. Since the infant cry is one of the only means that an infant has for communicating with its care-giving environment, it is thought that information regarding the state of an infant, such as hunger or pain, can be determined from cry vocalizations. In the open air trials, we found that our neural network approach created quality waveforms that conformed to the desired characteristics. The analysis of infant cry vocalization has been the focus of a number of efforts over the past thirty years. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. ![]()
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