Transient Resonance Scattering and Target Identification

1997 ◽  
Vol 50 (3) ◽  
pp. 131-148 ◽  
Author(s):  
G. C. Gaunaurd ◽  
H. C. Strifors
Keyword(s):  
Frequency Domain ◽  
Target Identification ◽  
Resonance Scattering ◽  
Convergent Series ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Transient Scattering ◽  
Transient Resonance ◽  
Active Classification

The article presents an overview of transient resonance scattering, emphasizing one of its most important applications—the active classification of sonar and radar targets. It discusses traditional, classical techniques such as the Watson-Sommerfeld method (WSM) to transform classical, and slowly convergent normal-mode series in the frequency domain, to rapidly convergent series in the domain of the complex generalization, λ, of the mode-order, n. In view of its analytical complexity and the advent of computers that can overcome slow convergence difficulties, the WSM is not as popular today as it once was. Its main advantage remains its ability to extract physical interpretations from the mathematical results. Resonance scattering focuses on the resonance spectral region of targets. Of these, the penetrable (ie, elastic or dielectric) ones are the subjects of main interest here, particularly those insonified/illuminated by (finite) pulses of various types. The authors describe the exact isolation and extraction of the resonances contained within the scattering cross-section of a penetrable target by subtraction of suitable, background, geometrical contributions. These backgrounds are often given by the solution for an identical, but impenetrable target. This seems to be the main usefulness of impenetrable target solutions in underwater acoustics, which, generally, are physically unrealistic idealizations. The resonances identify the target as its fingerprint. Examples are shown to illustrate various transient scattering phenomena in acoustics and electromagnetism. The article shows exactly how the broadband pulses emitted by an impulse sonar (or radar) extract a substantial number of resonances from the echoes of penetrable targets. Further, it is shown how these are actually used to identify all physical characteristics of various analyzed targets, thus, indeed identifying them. The application of a novel signal processing technique that analyzes the echoes in the joint time-frequency domain is examined. This shows much promise for target identification purposes. Many distributions of the Wigner-type were used by us to generate simulated and experimental echo-displays in time-frequency that show the advantages of the process. The present overview supplements two earlier ones [23, 48] on closely related subjects. The article includes 101 references.

scholarly journals Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3725
Author(s):  
Paweł Zimroz ◽  
Paweł Trybała ◽  
Adam Wróblewski ◽  
Mateusz Góralczyk ◽  
Jarosław Szrek ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Signal ◽  
Processing Technique ◽  
Underground Mine ◽  
Search And Rescue ◽  
Reference Spectrum ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Communication Device ◽  
Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

CARDIAC MURMUR ANALYSIS USING THE SHORT-TIME FOURIER TRANSFORM

2006 ◽  
Vol 06 (03) ◽  
pp. 273-284 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Fourier Transform ◽  
Processing Technique ◽  
Graphic Representation ◽  
Heart Valve Disease ◽  
Cardiac Auscultation ◽  
Short Time Fourier Transform ◽  
Signal Processing Technique ◽  
Cardiac Murmur ◽  
Time Frequency ◽  
Short Time

This paper describes a signal processing technique aimed at complementing cardiac auscultation in the detection of heart valve disease. The method provides a means for keeping objective records by analyzing the characteristics of the cardiac murmurs. The Short-time Fourier Transform (STFT) is used here to provide a graphic representation of the time-frequency information of the cardiac murmurs from eight different pathology cases. The graphic representation obtained shows the variation in frequency and intensity during the murmur. Some interesting observations show characteristic rising and falling tones, suggesting degrees of the pathology severity.

Target identification in the time-frequency domain

1996 ◽  
Author(s):  
Ismail I. Jouny ◽  
Passant V. Karunaratne ◽  
Moeness G. Amin
Keyword(s):  
Frequency Domain ◽  
Target Identification ◽  
Time Frequency

A Wavelet-Based Damage Detection Approach for Acousto-Ultrasonic In Situ Monitoring Systems

2011 ◽  
Vol 471-472 ◽  
pp. 809-814 ◽  
Author(s):  
Chin Kian Liew ◽  
Martin Veidt
Keyword(s):  
Damage Detection ◽  
Laminate Plate ◽  
Processing Technique ◽  
Guided Wave ◽  
Signal Processing Technique ◽  
Piezoceramic Actuator ◽  
Time Frequency ◽  
Wave Response ◽  
Detection Approach ◽  
Normalisation Factor

In this research, an advanced signal processing technique using wavelet analysis has been developed for a guided wave structural health monitoring system. The approach was applied for the detection of delamination in carbon fibre reinforced composites. A monolithic piezoceramic actuator was attached to a laminate plate for wave generation while laser vibrometry was used to facilitate the measurements of the wave response in a sensor network. This database of wave response was then processed using the continuous wavelet transform to obtain the positional frequency content. Transforms between damaged and undamaged states were compared to ascertain the presence of defects by evaluating the total energy of the time-frequency density function. Results show high damage detection indices depending on the location of the sensor and normalisation factor applied while there are positive indications that this methodology can be extended for damage characterisation.

Spectral balancing GPR data using time-variant bandwidth in the t-f domain

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. J19-J27 ◽  
Author(s):  
Nikos Economou ◽  
Antonis Vafidis
Keyword(s):  
Time Windows ◽  
Attenuation Factor ◽  
Amplitude Spectrum ◽  
Dominant Frequency ◽  
Processing Technique ◽  
Frequency Noise ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Text Filtering ◽  
Ground Penetrating

Ground-penetrating radar (GPR) sections encounter a resolution reduction with depth because, for electromagnetic (EM) waves propagating in the subsurface, attenuation is typically more pronounced at higher frequencies. To correct for these effects, we have applied a spectral balancing technique, using the S-transform (ST). This signal-processing technique avoids the drawbacks of inverse [Formula: see text] filtering techniques, namely, the need for estimation of the attenuation factor [Formula: see text] from the GPR section and instability caused by scattering effects that result from methods of dominant frequency-dependent estimation of [Formula: see text]. The method designs and applies a gain in the time-frequency ([Formula: see text]) domain and involves the selection of a time-variant bandwidth to reduce high-frequency noise. This method requires a reference amplitude spectrum for spectral shaping. It performs spectral balancing, which works efficiently for GPR data when it is applied in very narrow time windows. Furthermore, we have found that spectral balancing must be applied prior to deconvolution, instead of being an alternative technique.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

scholarly journals An Improved Hilbert–Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles

2021 ◽  
Vol 11 (7) ◽  
pp. 2974
Author(s):  
Ipshita Das ◽  
Mohammad Taufiqul Arif ◽  
Aman Maung Than Oo ◽  
Mahbube Subhani
Keyword(s):  
Wavelet Packet ◽  
Wood Properties ◽  
Dominant Frequency ◽  
Processing Technique ◽  
Natural Material ◽  
Signal Processing Technique ◽  
Frequency Bands ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reconstructed Signal

In this study, vibration based non-destructive testing (NDT) technique is adopted for assessing the condition of in-service timber pole. Timber is a natural material, and hence the captured broadband signal (induced from impact using modal hammer) is greatly affected by the uncertainty on wood properties, structure, and environment. Therefore, advanced signal processing technique is essential in order to extract features associated with the health condition of timber poles. In this study, Hilbert–Huang Transform (HHT) and Wavelet Packet Transform (WPT) are implemented to conduct time-frequency analysis on the acquired signal related to three in-service poles and three unserviceable poles. Firstly, mother wavelet is selected for WPT using maximum energy to Shannon entropy ratio. Then, the raw signal is divided into different frequency bands using WPT, followed by reconstructing the signal using wavelet coefficients in the dominant frequency bands. The reconstructed signal is then further decomposed into mono-component signals by Empirical Mode Decomposition (EMD), known as Intrinsic Mode Function (IMF). Dominant IMFs are selected using correlation coefficient method and instantaneous frequencies of those dominant IMFs are generated using HHT. Finally, the anomalies in the instantaneous frequency plots are efficiently utilised to determine vital features related to pole condition. The results of the study showed that HHT with WPT as pre-processor has a great potential for the condition assessment of utility timber poles.

Gear condition monitoring by a new application of the Kolmogorov—Smirnov test

2001 ◽  
Vol 215 (6) ◽  
pp. 653-661 ◽  
Author(s):  
F A Andrade ◽  
I I Esat ◽  
M N M Badi
Keyword(s):  
Condition Monitoring ◽  
Spur Gear ◽  
Processing Technique ◽  
Reference Distribution ◽  
Signal Processing Technique ◽  
Time Frequency ◽  
Vibration Signature ◽  
Kolmogorov Smirnov ◽  
Vibration Signatures ◽  
Ks Test

This paper introduces a new technique for the vibration condition monitoring of a set of spur gears. This technique, the Kolmogorov—Smirnov (KS) test, is based on a statistical comparison of two vibration signatures, which tests the ‘null hypotheses that the cumulative density function (CDF) of a target distribution is statistically similar to the CDF of a reference distribution’. In practice, the KS test is a time-domain signal processing technique that compares two signals and returns the likelihood that the two signals are statistically similar (i.e. have the same probability distribution function). Consequently, by comparing a given vibration signature with a number of template signatures for known gear conditions, it is possible to state which is the most likely condition of the gear under analysis. It must be emphasized that this is not a moment technique as it uses the whole CDF instead of sections of the CDF. In this work, the KS test is applied to the specific problem of direct spur gear condition monitoring. It is shown that this test not only successfully identifies the condition of the gear under analysis (brand new, normal, faulty and worn out), but also gives an indication of the advancement of the crack. Furthermore, this technique identifies cracks that are not identified by popular methods based on the statistical moment and/or time-frequency (TF) analysis and the vibration signature. This shows that, despite its simplicity, the KS test is an extremely powerful method that effectively classifies different vibration signatures, allowing for its safe use as another condition monitoring technique.

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