LPI Radar Signal Modulation Recognition with Feature Fusion Based on Time- frequency Transforms

2021 ◽  
Author(s):  
Shitong Li ◽  
Daying Quan ◽  
Xiaofeng Wang ◽  
Xiaoping Jin
Keyword(s):  
Feature Fusion ◽  
Radar Signal ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Time Frequency

scholarly journals Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2840
Author(s):  
Hubert Milczarek ◽  
Czesław Leśnik ◽  
Igor Djurović ◽  
Adam Kawalec
Keyword(s):  
Instantaneous Frequency ◽  
Early Warning Systems ◽  
Vital Role ◽  
Estimation Methods ◽  
Radar Signal ◽  
Nonlinear Frequency ◽  
Modulation Recognition ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Radar Signals

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

scholarly journals Multi-feature radar signal modulation recognition based on improved PSO algorithm

2019 ◽  
Vol 2019 (19) ◽  
pp. 5588-5592 ◽  
Author(s):  
Jing-Peng Gao ◽  
Liang-Xi Shen ◽  
Fang Ye ◽  
Shang-Yue Wang ◽  
Ran Zhang
Keyword(s):  
Pso Algorithm ◽  
Radar Signal ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Improved Pso

Signal Modulation Recognition based on Convolutional Autoencoder and Time-Frequency Analysis

2021 ◽  
Author(s):  
Xiangyu Wu ◽  
Jing Zhang ◽  
Changbo Hou ◽  
Guowei Liu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Convolutional Autoencoder

scholarly journals Radar Signal Modulation Recognition Based on Deep Joint Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 48515-48528 ◽  
Author(s):  
Dongjin Li ◽  
Ruijuan Yang ◽  
Xiaobai Li ◽  
Shengkun Zhu
Keyword(s):  
Radar Signal ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Joint Learning

Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition

2021 ◽  
pp. 1-1
Author(s):  
Shunjun Wei ◽  
Qizhe Qu ◽  
Xiangfeng Zeng ◽  
Jiadian Liang ◽  
Jun Shi ◽  
...  
Keyword(s):  
Radar Signal ◽  
Modulation Recognition ◽  
Signal Modulation

Frequency Hopping Signal Modulation Recognition Based on Time-Frequency Analysis

2021 ◽  
Author(s):  
Jing Zhang ◽  
Changbo Hou ◽  
Yun Lin ◽  
Jie Zhang ◽  
Yongjian Xu ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Frequency Hopping ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals Radar Signal Modulation Recognition Based on Sep-ResNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7474
Author(s):  
Yongjiang Mao ◽  
Wenjuan Ren ◽  
Zhanpeng Yang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Morphological Processing ◽  
Radar Signal ◽  
Modulation Recognition ◽  
Time Frequency ◽  
Low Snr ◽  
Modulated Signals ◽  
Intelligent Recognition

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.

Signals Modulation Recognition Based on Efficient Attribute Reduction with Neighborhood Rough Set

2012 ◽  
Vol 220-223 ◽  
pp. 2301-2307
Author(s):  
Ying Zheng Han ◽  
Juan Ping Wu ◽  
Xiao Fang Liang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Recognition Rate ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signals ◽  
Time Frequency ◽  
Feature Parameters ◽  
Neighborhood Rough Set ◽  
Propagation Network

The purpose of communication signals automatic modulation recognition is to judge signal modulation styles and estimate signal modulation parameters on the precondition of unknown modulation information. According to the seven kinds communication modulation signals studied in this paper, select a group of feature parameters based on the time-frequency characteristics of communication signals. The fast algorithm for attribute reduction based on neighborhood rough set using feature selection is introduced in detail. Then, using back propagation network as classification instruments to identify signals. The simulation shows that the method can not only reduce the number of feature parameters, but also improve the recognition rate.

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