Neural network radar manual#
Meanwhile, employing CNN as the encoder module means that manual intervention will not be needed anymore, which makes the recognition process more reasonable and reliable.Įxisting recognition methods of LPI radar signal modulations are mostly based on time-frequency analysis and DL. Because compared with any single domain method mentioned above, the time-frequency technique performs well in the aspect of anti-noise. Therefore, the method of combining TFIs and CNN stands out from all these approaches.
Neural network radar series#
It has already been proved that compared with other models of DL, such as Stacked AutoEncoder (SAE) and Deep Belief Network (DBN), CNN has a better performance in many areas such as time series prediction, target detection, and object identification. Especially for the DL methods, more and more attentions have been paid to them recently, due to their superb performance. And for the classifier, both traditional machine learning (ML) and prevalent deep learning (DL) are widely applied. From the perspective of features, most methods can be summarized into four classes: time-domain methods, frequency-domain methods, time-frequency domain methods, and transform-domain methods. Most exiting methods about LPI radar signal modulation recognition involve two key processes, which are feature extraction and signal classification.
Neural network radar how to#
To improve the cognition ability of reconnaissance equipment, how to precisely identify LPI radar signals in a harsh electromagnetic environment becomes a hot spot in electronic warfare systems. Due to the properties of low power, high resolution, large bandwidth, frequency changing, and so on, it is tough for traditional electronic reconnaissance methods to estimate parameters of received signals exactly, which means different modulation types of LPI radar signals cannot be recognized accurately. LPI radar prevents the non-cooperative receiver from intercepting and detecting its signals by transmitting a special waveform. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. This paper proposes an automatic recognition method for different LPI radar signal modulations. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed.
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