Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
W. L. Lu ◽  
J. W. Xie ◽  
H. M. Wang ◽  
C. Sheng
Keyword(s):  
Frequency Analysis ◽  
Simulated Data ◽  
Radar Signal ◽  
Electronic Warfare ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Radar Signals ◽  
Electronic Intelligence

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

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.

An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

2012 ◽  
Vol 198-199 ◽  
pp. 803-807
Author(s):  
Feng Li Wang ◽  
Shu Lin Duan ◽  
Hong Tao Gao
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Local Energy ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Process Systems ◽  
Frequency Space ◽  
Time Frequency ◽  
Stationary Signals ◽  
Local Wave

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

Detection and Parameter Extraction of Low Probability of Intercept Frequency Hopping Signals using the Spectrogram and the Reassigned Spectrogram

2020 ◽  
pp. 11-20
Author(s):  
Daniel L. Stevens
Keyword(s):  
Frequency Analysis ◽  
Parameter Extraction ◽  
Frequency Hopping ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Low probability of intercept radar signals, which are often problematic to detect and characterize, have as their goal ‘to see and not be seen’. Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing these low probability of intercept radar signals. Although these classical time-frequency analysis techniques are an improvement over existing Fourier-based techniques, they still suffer from a lack of readability –which can be caused by poor time-frequency localization (such as the spectrogram), which may in turn lead to inaccurate detection and parameter extraction. In this study, the reassignment method, because of its ability to improve time-frequency localization, is proposed as an improved signal analysis technique to address the poor time-frequency localization deficiency of the spectrogram. This paper presents the novel approach of characterizing low probability of intercept frequency hopping radar signals through utilization and direct comparison of the spectrogram versus the reassigned spectrogram.

Sign in / Sign up

Export Citation Format

Share Document