Radar Echo Parameter Estimation Using Sparse Time-Frequency Analysis Method

2014 ◽  
Vol 543-547 ◽  
pp. 2229-2233
Author(s):  
Hao Chen ◽  
Jun Hai Guo
Keyword(s):  
Parameter Estimation ◽  
Frequency Analysis ◽  
Computational Cost ◽  
Velocity Estimation ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Adaptive Data Analysis

The echoes of pulse radar from maneuvering targets are amplitude modulation and frequency modulation (AM-FM) signal. At present, the methods of estimating parameters of AM-FM signal are time-frequency analysis method, empirical mode decomposition and empirical wavelet transform based adaptive data analysis methods. This paper takes the idea of intrinsic mode function in guessing the initial phase, and applies the newly developed sparse time-frequency analysis method in AM-FM signal parameter estimation. Simulation results show that the estimating performance of this method in AM-FM signal is good under different SNR and it has low computational cost, and this method is applicable in target acceleration and velocity estimation.

Application of the Empirical Mode Decomposition Time-Frequency Analysis Method Based on Pipeline Leak Detection

2014 ◽  
Vol 602-605 ◽  
pp. 2213-2216
Author(s):  
Jing Tian
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Traditional Method ◽  
Leak Detection ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Leak Location ◽  
Mode Decomposition ◽  
Decomposition Time

In pipeline leak detection, collected fault signal will inevitably influenced by all kinds of industrial noise, sometimes even the useful signals submerged by the noise, so it causes a huge problem for leak location. Regarding this problem, the Empirical Mode Decomposition was used to analyze the signal instead of the traditional method of Time-Frequency analysis method. Compared with the traditional method, this method can not only get tid of noise, but also analyze the fault signal more accurately, which would help to improve the accuracy of the pipeline leak detection further.

scholarly journals Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110183
Author(s):  
Wuwei Feng ◽  
Xin Chen ◽  
Cuizhu Wang ◽  
Yuzhou Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Wire Bonding ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Wire Bonding

Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

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