Synthesis of a Digital Clutter Generator for Radar Applications

In seismic engineering, the earthquake-induced ground motion is generally represented in the form of pseudo-acceleration or displacement response spectra. There are, however, situations in which the response spectrum is not considered appropriate, and a fully dynamic analysis is required. In this case, the most effective approach is to define artificial spectrum-compatible stationary accelerograms, which are generated to match the target elastic response spectrum. So a Power Spectral Density (PSD) function is derived from the response spectrum. However, the above approach possesses the drawback that the artificial accelerograms do not manifest the variability in time and in frequency observed from the analysis of real earthquakes. Indeed, the recorded accelerograms can be considered sample of a fully non-stationary process. In this study a procedure based on the analysis of a set of accelerograms recorded in a chosen site to take into account their time and frequency variability is described. In particular the generation of artificial fully non-stationary accelerograms is performed in three steps. In the first step the spectrum-compatible PSD function, in the hypothesis of stationary excitations, is derived. In the second step the spectrum-compatible Evolutionary Power Spectral Density (EPSD) function is obtained by an iterative procedure to improve the match with the target response spectrum starting from the PSD function, once a time-frequency modulating function is chosen. In the third step the artificial accelerograms are generated by the well-known Shinozuka and Jan (J Sound Vib 25:111–128, 1972) formula and deterministic analyses can be performed to evaluate the structural response. Once the EPSD spectrum-compatible function is derived, a method recently proposed by the authors (Muscolino and Alderucci in Probab Eng Mech 40:75–89, 2015), is adopted to evaluate the EPSD response function of linear structural systems subjected to fully non-stationary excitations by very handy explicit closed-form.

Time-resolved scalar measurements in a confined turbulent premixed flame

In order to simulate the non-Gaussian fluctuating wind pressure with prescribed lower-order moments and its target power spectral density (PSD) function, the following framework is proposed in the present paper: Firstly, the PSD function of fluctuating wind pressure is deduced rigorously according to the relationship between the wind pressure and velocity; Then, an efficient yet practical method by which the fluctuating wind pressure may be reproduced with the designated skewness and kurtosis is presented based on the derived PSD function, Johnson translator system, and digital filter; Finally, in order to corroborate the feasibility and correctness of the present methodology, a numerical example of simulating one-dimensional univariate non-Gaussian fluctuating wind pressure is taken into consideration. Numerical results indicate that the statistical parameters, such as the skewness and kurtosis, of the simulated fluctuating wind pressure are in remarkably agreement with their targets. Likewise, the PSDs of the simulated records and target show considerably good agreement with each other.

Reduction of leakage and increase of resolution in power spectral density and coherence functions

Signal processing of flutter flight test data enables verification of aircraft flutter design, and the signal from a flutter flight test excited by atmospheric turbulence is a particularly important form of the flutter test. Owing to the randomness of atmospheric turbulence excitation, multi-channel analysis of turbulence responses at various positions in the same component can improve the analytical accuracy of flutter signal processing. The relationship between the maximum singular value of the multi-channel turbulence response power spectral density matrix and the system self-power spectral density function is elucidated herein using a frequency domain decomposition method. However, there is a contradiction in the power spectral density function between the spectral line density and the spectral line smoothing calculated based on the periodogram of the frequency domain decomposition. By applying an autoregressive spectral model, the power spectral density function of the turbulence response is calculated to achieve spectral line smoothing and sufficient spectral line density. Additionally, the power spectral density function is then used to construct the power spectral density function matrix of the multi-channel turbulence response, and the maximum singular value curve is calculated based on the singular value decomposition of each spectral pin. Finally, the modal parameters of the turbulence response signal are estimated via multi-modal frequency domain fitting. The developed approach is validated based on simulations and flutter flight test turbulence response signals.

An efficient and precise time-frequency analysis method for real-time ocean bottom seismometer (RTOBS) data in the South China Sea (SCS) is presented. Overcoming the limitations of conventional methods, the method involves temporal segmentation, unique frequency octaves, and Fourier transforms to generate power spectral density (PSD) and probability density function profiles. The method demonstrates superior precision, computational efficiency, and full-bandwidth (0 to Nyquist) capability compared to traditional techniques, as validated through theoretical and empirical evaluations. Applied to SCS RTOBS data, it unveils temporal PSD variations, shedding light on underwater noise sources like earthquakes, offshore blasting, ship-induced disturbances, and tidal effects. Establishing background noise levels in the SCS supports noise source categorization and ocean environment monitoring. Furthermore, comparing onshore and offshore seismic stations advances interdisciplinary research, fostering a comprehensive understanding of acoustics and seismology in the region.

The problem of conditional simulation of non-Gaussian stochastic processes and fields has gained a significant interest recently because of its applications in many fields, such as wind engineering, ocean engineering, and soil engineering. In this paper, the support vector machines (SVM) approach is developed for the conditional simulation of non-Gaussian stochastic processes and fields. To show the advantages of the presented method, the conditional simulation of non-Gaussian fluctuating wind pressures is carried out by using SVM and artificial neural networks (ANN). SVM considers three kinds of kernel function, such as linear function, Gaussian radial basis function, and exponential radial basis function, whereas ANN employs back-propagation and generalized regression. In machine learning of these artificial intelligences, two ways (interpolation and extrapolation) are employed to train finite non-Gaussian samples. The feasibility and validity of these algorithms are evaluated through the correlation coefficients, root mean square errors, skewness errors, and kurtosis errors between simulated samples and target samples and probability density functions (PDF), power spectral density (PSD) functions, and autocorrelation functions (ACF) of the simulated non-Gaussian fluctuating wind pressures versus their corresponding targets. The results show that the accuracy and effectiveness of SVM with an appropriate kernel function are superior to the back-propagation neural network (BPNN) and generalized regression neural network (GRNN). Furthermore, the advantage of the presented SVM approach is very obvious when the trained non-Gaussian samples are few.

A priori knowledge of the clutter statistics is required in maximizing a radar detection range. Much research has been done in analyzing various types of land clutter terrains including urban, forest and crops terrains. However, analysis of palm trees terrain has not been addressed in the open literature. Palm trees have a unique structure, which make them different from other type of trees. Date farms usually consists of date palm trees and scattered small buildings. In this paper, the clutter statistics of date farms terrain were analyzed. The amplitude Probability Distribution Function (PDF) was fitted to different distributions and the Power Spectral Density (PSD) was analyzed using Chan's model. It was found that the amplitude PDF of date farms terrain can be best modelled by a Log-normal distribution. The clutter statistics properties, which can be estimated from measured clutter, can be used in generating simulated clutter samples. Simulated clutter can be used in assessing detection algorithms in the presence of clutter. Generating simulated land clutter with similar statistics to the measured clutter is a challenge. In this paper, simulated clutter samples were generated using a PSD based model. The radar clutter simulator generated complex samples with a PSD similar to the measured clutter, but the amplitude PDF was different. The amplitude PDF of the measured clutter was observed to have a longer tail compared to the simulated clutter.

Construction and installation of the quarter-scale cold model of the Tidd pressurized bubbling bed combustor is complete. The cyclone and filter box have been installed, and the bed is fully instrumented. A data acquisition system has been set up to obtain time-varying pressure measurements from the cold model. Additional software to reduce orifice plate pressure drop measurements into flows, was completed. The capability to calculate the power spectral density (PSD) and probability density function (PDF) for the pressure fluctuation data has also been established. The last major work item remaining before data can be obtained from the model is to segregate the cold bed particles to obtain the required mean particle diameter and size distribution. As mentioned in the last quarterly report, a linear low-density polyethylene produced by Union Carbide was chosen as the cold bed material. Union Carbide distributes the granular polyethylene in 1200 lb{sub m} boxes. The large volume of particles makes the use of standard laboratory sieving methods impractical. We are currently attempting to segregate the particles by fluidizing them in the cold model. Different size particles will be carried out of the bed at different superficial velocities depending on their terminal velocities. The elutriated particles can then be collected using the cyclone separator.

The present paper focuses on reliability analysis of structural systems under ground motion acceleration taking into account the inherent random nature of the excitation as well as epistemic uncertainties affecting the definition of its spectrum. Specifically, seismic excitation is modelled as a zero-mean stationary Gaussian random process fully characterized by an imprecise power spectral density (PSD) function i.e. with interval parameters. The ranges of such interval parameters are determined by analysing a large set of accelerograms recorded on rigid soil deposits. To discard outliers, the Chauvenet’s Criterion is applied iteratively. The proposed imprecise PSD function may be viewed as representative of the actual accelerograms recorded on rigid soil deposits. Reliability assessment is carried out by incorporating the imprecise PSD function of ground motion acceleration into the formulation of the classical first-passage problem using Vanmarcke’s failure criterion. Due to imprecision of the excitation, the reliability function and the fractile of order p of the selected extreme value response process turn out to have an interval nature. Thus, the aim of reliability analysis is the evaluation of the bounds of such functions which define the range of structural performance. Numerical results show that neglecting imprecision of the PSD may lead to significant overestimation of the safety level.

Poster 82: Rate of Change in Surface Electromyography Median Frequency to Measure Muscle Fatigue

The performance of various discrete adaptive delta modulation (ADM) systems in the presence of random signal input is analyzed. The ADM systems considered are (a) Winkler system, (b) Abate (1967) system, (c) Bosworth and Candy (1968) system, and (d) Tazaki system (1977). It is assumed that the input random signal has a slope which varies randomly having Gaussian, Rayleigh and exponential probability density function (PDF) and has rectangular and triangular power spectral density (PSD). For an input random signal with specific PSD and PDF of its slope, the SNR has been calculated and plotted against the signal power. A performance evaluation has been made and summarized for the four systems above. >

The purpose of this paper is to provide an insight into low frequency divergent noises with spectral density |f|?, where ? ? -1, and into their effect on physical measurements, with special reference to 1/|f| noise. This class of noise is widespread in nature, and it presents unique limitations to the measurement accuracy. In an attempt to present a picture of this class of noise with regard to the measurements of observable physical quantities, the questions about generation of noise, its divergence, correlation properties and measurements of variance are discussed. A statistical model for generation of low frequency divergent noises is used to consider the divergence problem in both the frequency and time domain. It is shown that 1/|f| noise is weakly divergent, and that power limitation presents no reason to impose a low frequency limit within time intervals observable in nature. Correlation properties are discussed in terms of the time-dependent correlation function, using an ideal impulse response which generates low frequency noise from white noise. Two general models for generation of 1/|f| noise are summarized and discussed. Generation of 1/|f| noise from white noise over a limited frequency range by distributed and lumped-parameter filters is described. It is shown that the variance (i.e. mean square noise) is determined by the frequency limits of the observation method.

Stochastic modeling of the seismic excitation for structural dynamics purposes

A volume integral method for the calculation of Doppler ultrasound spectral power density (spd) functions is described. Axisymmetric flow in a circular tube with a power law velocity profile is assumed. The spd function is regarded as a probability density function for scatterer velocity, and the assumptions under which this is justified are considered. It is shown that the spd function is independent of Doppler angle except in the presence of wall reflection effects. A coordinate system centered on the beam is used and this enables the integrals to be easily formulated for arbitrary beams. Irregularly shaped and nonuniform beams can be treated. For the common flow and beam patterns, which exhibit symmetry, the volume integrals can often be reduced to a single integral and evaluated directly. The method is applied and the spectra are calculated for various different cases. Results are obtained for uniform rectangular and circular insonating beams, and for nonuniform beams with Gaussian, jinc, and sinc profiles. The effects of narrow beams and wall reflection are shown. The method may be readily applied to other beam and flow patterns, and extension to more complicated situations is also discussed.