Sökning: "Low Probability of Intercept Radar"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Low Probability of Intercept Radar.

1. 1. Parameter Estimation of LPI Radar in Noisy Environments using Convolutional Neural Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Filip Appelgren; [2021]
   Nyckelord :Deep Learning; LPI; Radar; CNN; Parameter estimation; Djupinlärning; LPI; Radar; Faltningsnätverk; Parameterestimering;

   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

3. 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)

   Författare :Måns Ekelund; [2021]
   Nyckelord :LPI radar; Uncertainty Quantification; Deep Learning; Bayesian Neural Networks; Deep Ensembles; LPI radar; Osäkerhetsskattning; Djupinlärning; Bayesianska neurala nätverk; Djupa ensembler;

   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

4. 3. LPI waveforms for AESA radar

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Fasta tillståndets elektronik

   Författare :Andreas Sjöberg; [2020]
   Nyckelord :Radar LPI waveform AESA;

   Sammanfattning : 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

5. 4. Representation Learning for Modulation Recognition of LPI Radar Signals Through Clustering

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mila Grancharova; [2020]
   Nyckelord :Clustering; Representation Learning; Semi-Supervised Learning; Low Probability of Intercept Radar; Automatic Modulation Recognition; Klustring; semiövervakad inlärning; representationsinlärning; Low Probability of Intercept radar; automatiserad modulationsigenkänning;

   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

7. 5. Noise Robustness of Convolutional Autoencoders and Neural Networks for LPI Radar Classification

   Master-uppsats, KTH/Matematisk statistik

   Författare :Gustav Norén; [2020]
   Nyckelord :LPI radar; CNN; autoencoder; noise robustness; denoising; LPI radar; CNN; autoencoder; brustålighet; avbrusning;

   Sammanfattning : 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