Radar Signal Modulation Type Recognition Algorithm Based on Time-Frequency Image and Transfer Learning

2021 ◽  
Vol 10 (03) ◽  
pp. 113-126
Author(s):  
军见 苑
Keyword(s):  
Transfer Learning ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Signal Modulation ◽  
Time Frequency ◽  
Modulation Type

scholarly journals An Intra-Pulse Modulation Type Recognition Algorithm for Radar Signals Based on the Improved Residual Network

2021 ◽  
Vol 2050 (1) ◽  
pp. 012009
Author(s):  
Fan Wang ◽  
Yifeng Huang ◽  
Ming Zhu ◽  
Jun Tang ◽  
Zhaohong Jia
Keyword(s):  
Loss Function ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Residual Network ◽  
Pulse Modulation ◽  
Time Frequency ◽  
Signal Features ◽  
Modulation Type ◽  
Radar Signals

Abstract For purpose of solve the problem of poor discrimination and robustness of intra-pulse signal features extracted by the traditional methods, this paper proposes a radar signal intra-pulse modulation type recognition algorithm based on the improved residual network. Firstly, one-dimensional time-domain radar signal is converted into two-dimensional time-frequency image by Smoothing Pseudo Wigner-Ville Distribution; Then the time-frequency image is preprocessed; ResNet-50 network is chosen as the framework. In order to retain the feature map information as much as possible, the convolution kernel is increased in the residual module. The cross entropy loss function and the center loss function are used as the loss function to speed up the convergence of the network. The improved residual network is used to realize the intra-pulse modulation type recognition of radar signal. The simulation experiments show that when the SNR is -14dB, the overall average recognition accuracy of the improved algorithm for eight kinds of radar signals (CM, LFM, NLFM, BLFM, BPSK, QPSK, OPSK, LFM+BPSK) can reach 97.29%, which shows the effectiveness.

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 Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

2021 ◽  
Vol 13 (6) ◽  
pp. 1064
Author(s):  
Zhangjing Wang ◽  
Xianhan Miao ◽  
Zhen Huang ◽  
Haoran Luo
Keyword(s):  
Millimeter Wave ◽  
Autonomous Vehicles ◽  
Texture Features ◽  
Heterogeneous Data ◽  
Environmental Perception ◽  
Radar Signal ◽  
Time Frequency ◽  
Fusion Data ◽  
Wave Radar ◽  
Automation Equipment

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

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