FEATURE EXTRACTION OF RADAR MULTIPLE-TARGET ECHOES USING WAVELET PACKET TRANSFORM WITH THE BEST BASES

2003 ◽  
Vol 17 (01) ◽  
pp. 127-150 ◽  
Author(s):  
SHOUYONG WANG ◽  
GUANGXI ZHU ◽  
YUAN Y. TANG
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Time Varying ◽  
Multiple Target ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Entropy Criterion ◽  
Positive Results

Extraction of effective features plays a key role in pattern recognition. A large number of patterns, such as speech, radar signals, earthquake signals, handwriting, etc. are of non-stationary signals or exhibit time-varying behavior. The features of these patterns are often located in both the time and frequency domains. The traditional methods fail to extract such kind of features. Fortunately, wavelet packet transform (WPT) can provide an arbitrary time-frequency decomposition for the signals, because a wavelet packet (WP) library contains many WP bases, which can handle the different components of a signal. Therefore, by selecting a suitable basis, which is called "best basis", the effective features can be extracted. In this paper, three criteria are used to select the best WPT basis, namely: (1) distance criterion, (2) divergence criterion and (3) entropy criterion. Three algorithms to implement the above criteria are also provided. Experiments are conducted and the positive results are obtained.

Analysis for Power System Transient Harmonics Based on Wavelet Packet Transform

2013 ◽  
Vol 415 ◽  
pp. 241-244
Author(s):  
Rong Hui Liu ◽  
Ai Qang Pan ◽  
Xiu Yang
Keyword(s):  
Wavelet Transform ◽  
Power Systems ◽  
Power System ◽  
Harmonic Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Time Varying ◽  
Analysis Method ◽  
Frequency Decomposition ◽  
Harmonic Analysis Method

In this paper, the principles of wavelet transform and wavelet packet transform were presented. The wavelet packet transform had good characteristics of uniform frequency decomposition, and thus transient harmonic analysis method was proposed based on wavelet packet transform. Finally, transient interharmonic, time varying harmonic and transient oscillation signals in power systems were simulated with Matlab emulator. The perfect results of simulation show that the presented method can accurately detect the transient harmonics, which provides support for harmonic analysis in power systems.

scholarly journals A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform

2019 ◽  
Vol 9 (4) ◽  
pp. 777 ◽  
Author(s):  
Gaoyuan Pan ◽  
Shunming Li ◽  
Yanqi Zhu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Time Varying ◽  
Analysis Method ◽  
Frequency Correlation ◽  
Time Frequency ◽  
Time Series Decomposition ◽  
S Transform ◽  
Stationary Signals ◽  
Correlation Analysis Method

Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time–frequency (TF) correlation analysis method of time series decomposition (TD) derived from synchrosqueezed S transform (SSST) is proposed in this paper. First, the two-dimensional time–frequency matrices of the signals is obtained by synchrosqueezed S transform. Second, time series decomposition is used to transform the matrices into the two-dimensional time–time matrices. Third, a correlation analysis of the local time characteristics is carried out, thus attaining the time–frequency correlation between the signals. Finally, the proposed method is validated by stationary and non-stationary signals simulation and is compared with the traditional correlation analysis method. The simulation results show that the traditional method can obtain the overall correlation between the signals but cannot reflect the local time and frequency correlations. In particular, the correlations of non-stationary signals cannot be accurately identified. The proposed method not only obtains the overall correlations between the signals, but can also accurately identifies the correlations between non-stationary signals, thus showing the time-varying and frequency-varying correlation characteristics. The proposed method is applied to the acoustic signal processing of an engine–gearbox test bench. The results show that the proposed method can effectively identify the time–frequency correlation between the signals.

EEG Analysis

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Marco Congendo ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Matching Pursuit ◽  
Effective Connectivity ◽  
Eeg Signals ◽  
Model Decomposition ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Artifact Rejection ◽  
Transient Events

This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

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 An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Shen Jinxing
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Good Time ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Nonlinear Dimensionality Reduction ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Fault Type ◽  
Complex Wavelet

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

Intellectual Gear Fault Detection Based on Wavelet Time-Frequency Analysis

2013 ◽  
Vol 373-375 ◽  
pp. 762-769 ◽  
Author(s):  
Juan Li Zhou
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Gear System ◽  
Support Vector ◽  
Rotating Speed ◽  
Time Frequency ◽  
Fault Recognition ◽  
Vector Machines ◽  
Gear Fault

In this paper, wavelet packet transform and support vector machines are used to detect gear system faults. Testing signals were obtained by measuring the vibration signals of gear system at different rotating speed for different faults. Vibration feature signals were analyzed using wavelet de-noising. By using wavelet packet transform (WPT), signals were decomposed into different frequency bands. the fault detection is used for calculation of energy percents of every frequency. All these were used for fault recognition using Support vector machine (SVM). SVM and neural network transform results were compared. The research indicates that the de-noised signal is superior to the original one. When dealing with various signals, such as Multi-Faults, the diagnosis identification rates are over 92%. This method can be effectively used not only in engineering diagnosis of different faults of gear system, but also for other machinery fault style classification.

State Monitoring of Lathes Based on Load Current Signal

2012 ◽  
Vol 201-202 ◽  
pp. 758-762
Author(s):  
Yue Ping Yu ◽  
Guang Lin Yu ◽  
Hong Bin Li ◽  
Guo Fu Li
Keyword(s):  
Wavelet Packet ◽  
Wavelet Denoising ◽  
Wavelet Packet Transform ◽  
Current Signal ◽  
State Monitoring ◽  
Denoising Method ◽  
Load Current ◽  
Time Frequency ◽  
Characteristic Quantity ◽  
Motor Load

According to the characteristics of machine tools such as complex driving chain ,weak signal and enclosed housing,this paper takes horizontal lathes as study objects and selects current signal which is easy to sample as the analytical signal.We collect motor load current signals of idling, cylindrical cutting and end cutting processing state in the experiment to process the condition monitoring based on wavelet denoising and wavelet packet transform. We take advantage of the threshold denoising method to reduce noise of load current signal.Then we use time-frequency analysis methods of wavelet packet transform to extract state characteristic quantity and outstand useful information.So in this paper we monitor the working state of lathes based on the unique advantages of wavelet denoising and wavelet packet transform, and this method can be widely used in various fields of state monitoring.

Handwritten Signatures Verification Through Their Acoustic Patterns Based on the Discrete Wavelet-Packet Transform and Semantic-Matching Classifiers

2016 ◽  
Vol 10 (04) ◽  
pp. 557-567 ◽  
Author(s):  
Daniel Angelotti Armiato ◽  
Yuzo Yano ◽  
Vinícius Zani de Faveri ◽  
Rodrigo Capobianco Guido
Keyword(s):  
State Of The Art ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Semantic Matching ◽  
Biometric Authentication ◽  
Discrete Wavelet Packet Transform ◽  
Time Frequency ◽  
Non Invasive ◽  
Hand Shape

Biometric authentication based on fingerprints, voice, hand shape, facial measurements and iris analysis, among others, are quite common nowadays. In a similar manner, the analysis of acoustic patterns generated during the friction between pen and paper at the time a person subscribes has been shown to be a feasible, adequate, and non-invasive alternative to those techniques. An interesting implementation for such an approach, which is described in this paper, is based on the association of the time-frequency analysis supported by the discrete wavelet-packet transform with one of two pattern-matching classifiers, namely Euclidian norma and an original scoring equation derived from correlation, acting semantically. Valuable results were obtained during the tests, motivating further research. The proposed technique is novel on literature, offering a contribution to the state-of-the-art.

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