Results 31 to 40 of about 1,098 (282)
Analyzing radar emitter signals with membrane algorithms
Mathematical and Computer Modelling, 2010 The analysis of radar emitter signals is a critical process in modern electronic reconnaissance systems. This paper proposes the application of a modified variant, called MQEPS, of the quantum-inspired evolutionary algorithm based on P systems (QEPS) to the time-frequency atom decomposition for analyzing radar emitter signals.
Gexiang Zhang, Chunxiu Liu, Haina Rong
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Approximate maximum likelihood estimation of two closely spaced sources [PDF]
, 2014 The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is
Chaumette, Eric +2 more
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Radar Emitter Signal Analysis with Estimation of Distribution Algorithms
Journal of Networks, 2013 This paper proposes a novel approach (short for iEDA/TFAD) based on estimation of distribution algorithms and time-frequency atom decomposition for analyzing radar emitter signals. In iEDA/TFAD, an improved estimation of distribution algorithm combining Gaussian and Cauchy probability models is presented to implement time-frequency atom decomposition ...
Haina Rong, Jixiang Cheng, Yuquan Li
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Radar Emitter Signals Recognition and Classification with Feedforward Networks
Procedia Computer Science, 2013 AbstractA possible application of neural networks for timely and reliable recognition of radar signal emitters is investigated. In particular, a large data set of intercepted generic radar signal samples is used for investigating and evaluating several neural network topologies, training parameters, input and output coding and machine learning ...
Petrov, Nedyalko +2 more
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Identification of Radar Emitter Type with Recurrent Neural Networks
, 2022 S.21-25In this paper, we present a method for the identification of different multifunction radar emitter types. It is based on Long Short-Term Memory recurrent neural networks and a previously published hierarchical modelling approach.
Apfeld, S., Charlish, A., Ascheid, G.
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A new clustering and sorting algorithm for radar emitter signals
Journal of Physics: Conference Series, 2020 Abstract The traditional k-means clustering algorithm needs to set parameters manually in radar signal sorting application, which is sensitive to isolated points and prone to “batch” phenomenon, and the selection of initial clustering center has a direct impact on clustering effect.
MingWei Li +3 more
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Radar Emitter Signal Recognition Based on EMD and Neural Network
Journal of Computers, 2012 Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction.
Bin Zhu, Weidong Jin
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Detection and Separation of Multi-component Radar Emitter Signal [PDF]
Advances in Engineering Research, 2015 The problem of multi-component radar emitter signal processing is studied, an effective method based on time-varying filtering is proposed to detect and separate the multi-component signal in radar reconnaissance occasion. Each signal component in the time-frequency plane is detected by the region growing method which is an image segmentation algorithm.
Lijun Qi, Guoyi Zhang, Xiaofeng Wang
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Prototype‐based method for incremental radar emitter identification
IET Radar, Sonar & Navigation, 2023 With the widespread use of radars, different types of radar emitters are being used in the real electromagnetic environment. Radar emitter identification (REI) is an important technique in spectrum management.
Xiao Han +3 more
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, 2022 Radar emitter signal recognition under noisy background is one of the focus areas in research on radar signal processing. In this study, the soft thresholding function is embedded into deep learning network models as a novel nonlinear activation function,
Long Tan +4 more
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