Sökning: "Low Probability of Intercept radar"
Visar resultat 1 - 5 av 6 uppsatser innehållade orden Low Probability of Intercept radar.
1. Parameter Estimation of LPI Radar in Noisy Environments using Convolutional Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Low-probability-of-intercept (LPI) radars are notoriously difficult for electronic support receivers to detect and identify due to their changing radar parameters and low power. Previous work has been done to create autonomous methods that can estimate the parameters of some LPI radar signals, utilizing methods outside of Deep Learning. LÄS MER
2. Uncertainty Estimation for Deep Learning-based LPI Radar Classification : A Comparative Study of Bayesian Neural Networks and Deep Ensembles
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Deep Neural Networks (DNNs) have shown promising results in classifying known Low-probability-of-intercept (LPI) radar signals in noisy environments. However, regular DNNs produce low-quality confidence and uncertainty estimates, making them unreliable, which inhibit deployment in real-world settings. LÄS MER
3. LPI waveforms for AESA radar
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Fasta tillståndets elektronikSammanfattning : The purpose of low probability of intercept (LPI) radar is, on top of the standard requirements on a radar, to remain undetected by hostile electronic warfare (EW) systems. This can be achieved primarily by reducing the amount of radiated power in any given direction at all times and is done by transmitting longer modulated pulses that can then be compressed digitally in order to retain range resolution. LÄS MER
4. Representation Learning for Modulation Recognition of LPI Radar Signals Through Clustering
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Today, there is a demand for reliable ways to perform automatic modulation recognition of Low Probability of Intercept (LPI) radar signals, not least in the defense industry. This study explores the possibility of performing automatic modulation recognition on these signals through clustering and more specifically how to learn representations of input signals for this task. LÄS MER
5. Noise Robustness of Convolutional Autoencoders and Neural Networks for LPI Radar Classification
Master-uppsats, KTH/Matematisk statistikSammanfattning : This study evaluates noise robustness of convolutional autoencoders and neural networks for classification of Low Probability of Intercept (LPI) radar modulation type. Specifically, a number of different neural network architectures are tested in four different synthetic noise environments. LÄS MER