Modulation recognition method of time-frequency aliasing signals in single-channel

2020 ◽  
Author(s):  
Pengbo Li ◽  
Lin Qi ◽  
Junliang Wang
Keyword(s):  
Single Channel ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Time Frequency ◽  
Frequency Aliasing

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.

Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer

2021 ◽  
Author(s):  
John Henry Navarro-Devia ◽  
Dzung Viet Dao ◽  
Yun Chen ◽  
Huaizhong Li
Keyword(s):  
Titanium Alloys ◽  
Frequency Domain ◽  
Single Channel ◽  
End Milling ◽  
Cutting Parameters ◽  
Frequency Component ◽  
Spindle Speed ◽  
Triaxial Accelerometer ◽  
Vibration Signals ◽  
Time Frequency

Abstract Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

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