scholarly journals JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network

2021 ◽  
Author(s):  
Mengtao Zhu ◽  
Ziwei Zhang ◽  
Cong Li ◽  
Yunjie Li
Keyword(s):  
Parameter Estimation ◽  
Modulation Recognition ◽  
Cognitive Radar ◽  
Radar Signals

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 Iterative High-Accuracy Parameter Estimation of Uncooperative OFDM-LFM Radar Signals Based on FrFT and Fractional Autocorrelation Interpolation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3550 ◽  
Author(s):  
Yifei Liu ◽  
Yuan Zhao ◽  
Jun Zhu ◽  
Ying Xiong ◽  
Bin Tang
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
High Accuracy ◽  
Superior Performance ◽  
Spectral Leakage ◽  
Multiple Components ◽  
Orthogonal Frequency Division Multi ◽  
Accuracy Parameter ◽  
Finite Sampling ◽  
Radar Signals

To improve the parameter estimation performance of uncooperative Orthogonal Frequency Division Multi- (OFDM) Linear Frequency Modulation (LFM) radar signals, this paper proposes an iterative high-accuracy method, which is based on Fractional Fourier Transform (FrFT) and Fractional Autocorrelation (FA) interpolation. Two iterative estimators for rotation angle and center frequencies are derived from the analytical formulations of the OFDM-LFM signal. Both estimators are designed by measuring the residual terms between the quasi peak and the real peak in the fractional spectrum, which were obtained from the finite sampling data. Successful elimination of spectral leakage caused by multiple components of the OFDM-LFM signal is also proposed by a sequential removal of the strong coefficient in the fractional spectrum through an iterative process. The method flow is given and its superior performance is demonstrated by the simulation results.

scholarly journals Radar signals modulation recognition based on bispectrum feature processing

2021 ◽  
Vol 1971 (1) ◽  
pp. 012099
Author(s):  
Xinping Mi ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Denghua Hu
Keyword(s):  
Modulation Recognition ◽  
Feature Processing ◽  
Radar Signals
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