Improvement Factor Research Articles

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Effective Non-Stationary Clutter Suppression Method via Elevation Oblique Subspace Projection for Moving Targets Detection with a Space-Based Surveillance Radar

The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.

GPR Clutter Removal Based on Weighted Nuclear Norm Minimization for Nonparallel Cases

Ground-penetrating radar (GPR) is an effective geophysical electromagnetic method for underground target detection. However, the target response is usually overwhelmed by strong clutter, thus damaging the detection performance. To account for the nonparallel case of the antennas and the ground surface, a novel GPR clutter-removal method based on weighted nuclear norm minimization (WNNM) is proposed, which decomposes the B-scan image into a low-rank clutter matrix and a sparse target matrix by using a non-convex weighted nuclear norm and assigning different weights to different singular values. The WNNM method’s performance is evaluated using both numerical simulations and experiments with real GPR systems. Comparative analysis with the commonly used state-of-the-art clutter removal methods is also conducted in terms of the peak signal-to-noise ratio (PSNR) and the improvement factor (IF). The visualization and quantitative results demonstrate that the proposed method outperforms the others in the nonparallel case. Moreover, it is about five times faster than the RPCA, which is beneficial for practical applications.

Enhancing the ability of the square footing to resist positive and negative eccentric-inclined loading using an inclined skirt

Laboratory model tests were performed to investigate the behavior of shallow and inclined skirted foundations placed on sandy soil with R.D%=30 and the extent of the impact of the positive and negative eccentric-inclined loading effect on them. To achieve the experimental tests it was used a box of (600*600)mm cross-sectional and 600mm in height and a square footing of (50*50)mm and 10mm in thickness attached to the skirt with Ds=0.5B and various an angle of (10°,20°,30°). The results showed that using skirts leads to a significant improvement in load-carrying capacity and decreased settlement in addition when the skirt angle increased, the ultimate load improved. load-carrying capacity decreased with increasing eccentricity and load inclination For load inclination (Beta) 15° when the eccentricity changed from e=0.15B to e=0.05B the load improvement percentages were (323.2% to 263%) and (214% to 220%) and settlement reduction factor was (83% to 78%) and (62% to 58%) for positive and negative eccentric-inclined loading, respectively also the result showed the effect of positive on the reduction of soil-bearing capacity is more than negative. Increasing eccentricity increases the improvement percentage for positive eccentric-inclined load and decreases for the case of negative eccentric-inclined load. Increased skirt angle will increase the Improvement factor (IR) When the skirt angle increased from 10° to 30° for an improved foundation with load angles of 5°, 10°, and 15° the improvement factor (IR) increased from (2.53, 2.51, 2.4) to (3.45, 3.65, 3.97) and (2.43, 2.58, 2.54) to (4, 4.63, 5.3) for both negative and positive eccentric-inclined load respectively and settlement reduction factor for load angle 15° and skirt angle increase from 10° to 30° were 34% and 27% for positive and negative eccentric-inclined load respectively. the (IR) for positive eccentric-inclined load is more than the negative eccentric-inclined load for all cases, in addition, the skirt angle of 30° showed a significant improvement in the improvement factor (IR).

FACTORS FOR IMPROVEMENT OF TEACHING "Solid State Physics" IN GENERAL SECONDARY SCHOOL

FACTORS FOR IMPROVEMENT OF TEACHING "Solid State Physics" IN GENERAL SECONDARY SCHOOL

Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods.

Mechanical Behavior of Reinforced Embankment with Different Recycling Waste Fillers

The application of construction and demolition (C&D) waste and used tires in geotechnical engineering contributes to the demand of the sustainable development. This study mainly compared the mechanical behavior of pure sand embankment (PSE), C&D material embankment (CDME), sand-tire shreds mixture embankment (STSME) through a scale model test. Effects of tire shreds content, the first layer geocell reinforcement burial depth, geocell reinforcement depth and compaction degree on the bearing capacity of embankments were investigated. Moreover, embankment load-settlement ratio, the reinforced bearing capacity ratio, the bearing capacity improvement factor (IF), the embankment surface deformation, and the vertical earth pressure distribution inside the embankment were discussed. Results indicate that both C&D material fillers and sand-tire shreds mixture can improve the bearing capacity and stability of embankment slopes. However, C&D material is better than sand-tire shreds mixture in the improvement. The optimum value of tire shreds content is 5%. The reinforcement effect of geocell decreases with the increase of reinforcement depth. With the increase of the buried depth of the first layer, it first increases and then decreases. With the increase of compaction degree increases. The minimum earth pressure appears near the slope. The bearing capacity of CDME is greater than STSME. The ultimate bearing capacity of CDME under the action of the geocell is twice that of the unreinforced embankment.

Range-Ambiguous Clutter Suppression via FDA MIMO Planar Array Radar with Compressed Sensing

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression.

Numerical Analysis of the Carrying Capacity of a Piled Raft Foundation in Soft Clayey Soils

Piled raft foundations are a common type of foundation for high-rise buildings. Unlike shallow foundations, deep foundations (piles) pass through weak or soft soil deposits and can reach stiff soil or bedrock to support the weight of the structure. In this paper, the performance of a medium embedment depth piled raft foundation in soft soil is presented. A numerical model was developed and a parametric study was conducted in order to simulate the case of such a foundation system and to investigate its performance in soft clay. This parametric study investigated the effect of the geometry of a piled raft foundation and the stiffness ratio between the pile material and clay on the performance of the foundation system in soft soil. Additionally, the failure mechanism of such a foundation system under load was examined. An analytical model was developed to predict the ultimate carrying capacity based on the observed failure mechanism. A semi-empirical model is proposed for determining the Improvement Factor (IF) of a given soil, pile, and geometric condition. Findings of the study indicate that the performance of piled raft foundations on soft soils is significantly affected by the piles’ spacing. As the ratio S/D increases, the ultimate carrying capacity of a piled raft foundation decreases. However, when this ratio exceeds 10 (S/D> 10), piles have little or no effect on the ultimate carrying capacity of this foundation system. A piled raft foundation system fails by bearing at the base of the piles and also by shear at the side of the pile group on hyperbolic plans. Doi: 10.28991/CEJ-2022-08-04-01 Full Text: PDF

Opportunities for emerging market LLPs to improve the service quality of automotive parts exports in post-COVID-19

This research article proposes the service quality measures for four automotive lead logistics providers (LLPs) to improve the parts export business based at emerging markets such as India, China and Thailand. These measures have emerged from the pre-COVID-19 perception study on service quality from 117 global automotive professionals from auto original equipment manufacturers (OEMs). Multi-criteria decision-making (MCDM) framework and questionnaire survey method was applied. It concludes with the results of different methods such as service quality (SERVQUAL), importance-performance analysis (IPA) and improvement factor (IF), to explain the significance of export business and where to improve their service quality post-COVID-19. The result shows that the service quality factors of IPA model had wide-ranging reflection for LLPs to focus in comparison with SERVQUAL and IF to gauge the perception of automotive OEMs concerning the service quality of LLPs in the industry post-COVID-19.

Numerical modelling of geocell reinforced slope by use of plastic bottle waste

Nowadays, waste management should be one of the focuses in controlling a healthy and sustainable environment. It is undeniable that plastic bottle waste has covered the earth’s surface. One of the solutions to reduce the polluting impact is to use it as a geocell in the geotechnical field. The previous researchers more study about slope reinforcement using fabricated geocell. In this study, geocell is made by utilizing plastic bottle waste. Slope stabilization using geocell is a kind of internal stabilization. Other kinds of slope stabilization, such as using sheet piles and gravity walls, are external stabilization. The purpose of this study is to obtain an overview of the effect of the geocell reinforced slope. From experimental studies using plate load test, results obtained that geocell reinforce soil can increase the bearing capacity of the soil. Numerical modelling uses PLAXIS 2D modelled geocell reinforce slope as a slab that can spread the load widely over the reinforced area, also known as beam model. Young’s modulus of geocell-soil composite is obtained from experimental studies, empirical equation and validated using axisymmetry in PLAXIS 2D. The results of this study, generally geocell reinforce slope can restrain the slope from failure deformation by redistributing the loads in a large area. The significant effect is shown in sandy soil with a high improvement factor (IF), different to the reinforcement in clayey soil with a low improvement factor (IF). It implies that plastic bottle waste can be used as a geocell which can increase its economic value. Since it is applicable, it can reduce the pollution caused by plastic bottle waste.

Effect of 3D Geogrid and Glasgrid in the Bearing Capacity of Square Footing Over Soil Slope

Many civil engineering structures such as buildings, bridges, and road embankment etc. require the construction of foundations over soil slope. Reinforcement technique is a low-cost method of enhancing the load carrying capacity of such foundations. Over the past three decades, geosynthetics have been successfully used as a reinforcing material. In the present study, a series of small-scale model footing tests were conducted on the geosynthetics reinforced soil slope. A bi oriented 3D geogrid and glasgrid were used as a reinforcing material. The results obtained from laboratory model footing tests over a geosynthetics reinforced earth slope are analyzed and presented. Pressure settlement curves and improvement factor (IF) were studied by measuring the results of different settlement ratios. Various parameters like edge distance (D) and slope angle (β) were studied to check the effect of slope geometry. Some other parameters like optimum value of initial reinforcement depth (u), influence depth of reinforcement (d), and number of reinforcements (N) were also considered to check the effect of geosynthetic in the enhancement of load carrying capacity of footing over reinforced earth slope. Experimental results demonstrate that load carrying capacity of foundation resting over soil slope significantly improved by the placement of geosynthetics in the soil. On the basis of the experimental work, critical values of geosynthetic parameters for the optimum reinforcing effect are suggested.

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

In non-destructive testing and evaluation, the microwave-based synthetic aperture radar (SAR) imaging have shown great potential in the detection of defects on buried objects such as pipes. However, due to the pipe curvature and high stand-off distance when inspecting an insulated pipe, the useful defect information used to characterize the pipe image is lost as a result of low signal-to-noise ratio (SNR), resulting in a blurred and unfocused image. In this paper, we proposed a robust microwave-based SAR imaging using autofocus range-Doppler algorithm (RDA) for the inspection of an insulated pipe. Singular value decomposition is used to mitigate the effect of the insulation layer by removing dominant singular values that characterize the insulation layer, and the autofocus RDA is designed to refocus the SAR image using RDA residual refocusing. The SNR, improvement factor (IF), and squared error are used to evaluate the qualitative image information of the defect on the pipe. Experimental results showed the efficacy of the method in detecting defects on an insulated pipe, in particular, a significant reduction in the noise content of the image compared to the known SAR Omega-k algorithm. It was found that the autofocus RDA gave higher values of SNR and IF (3 dB and 6 dB) compared to the Omega-k algorithm (−1 dB and 2 dB), respectively.

A knowledge aided SPICE space time adaptive processing method for airborne radar with conformal array

Abstract For airborne conformal array radar, it is difficult to suppress clutter using conventional space-time adaptive processing (STAP) methods because the geometry results in range-dependent clutter and nonuniform spatial steering vector. In order to improve the performance of conformal array clutter suppression, a knowledge aided method based on semiparametric/sparse iterative covariance-based estimation STAP, named with KASPICE-STAP, is proposed here. The KASPICE-STAP method requires the knowledge of the clutter ridge spread of the testing range cell. Based on this knowledge, we can construct a dictionary matrix which consists of the clutter space-time steering vectors. Subsequently, by using the dictionary matrix, a relatively accurate covariance matrix of clutter plus noise can be calculated with the KASPICE-STAP method. The proposed approach can work efficiently in complex environments only with the data of cell under test. Compared with other existing sparse recovery STAP methods, the proposed method has global convergence properties and it does not require making any difficult selection of hyperparameters. The improvement factor (IF) curves and range-Doppler images demonstrate the effectiveness of the proposed method.

Importance Performance Analysis and Servqual Model: an Integrated Approach for Enhancing Customer Satisfaction

The paper aims to evaluate the service quality dimensions with reference to the banking sector along with the application of Importance Performance Analysis (IPA Framework) and Improvement Factor (IF). The study is qualitative in nature which follows the non-probability sampling method. In order to collect data, a well defined questionnaire consisting of close ended questions on service quality attributes, namely reliability, assurance, tangible, empathy and responsiveness have been prepared to record importance and performance level from customers. IPA Grid has been formed and Improvement Factor (IF) has also been calculated to look at the level of improvement needed for each attribute. This study brings out the application of SERVQUAL Model, IPA framework and Improvement Factor, which helps in identifying areas for strategic focus to develop future strategies. From the research perspective, this study supports the adaptation of the IPA Framework as an alternative/integrative framework for evaluating customer satisfaction.

Nonuniform Three-dimensional Configuration Distributed SAR Signal Reconstruction Clutter Suppression

This paper deals with the problem of clutter suppression in spaceborne distributed synthetic aperture radar (D-SAR) with nonuniform three-dimensional (3D) configuration geometry. In order to make a breakthrough of the configuration limitation of the traditional space time adaptive processing (STAP) based on uniform array and improve the inhomogeneous clutter suppression performance, this paper considers signal reconstruction technique using array interpolation to process the D-SAR signal. An array interpolation signal reconstruction method based on pitching-partition is derived then a signal reconstruction 3D-STAP clutter suppression method applied to nonuniform 3D configuration is proposed. In particular, the proposed method is compared with conventional methods and the performance analysis is carried out based on simulations. The improvement factor (IF) for clutter suppression is imported and reported as a benchmark on the clutter suppression effect.

Performance of adaptive optimal Doppler processors in heterogeneous clutter

The performance of adaptive Hsiao-optimal clutter-suppression filters operating in spatially varying clutter backgrounds is investigated. The case of practical adaptive estimation of the covariance matrix, and limiting cases where the estimate of the covariance matrix tends to the mean clutter covariance matrix, are considered. Three classes of nonhomogeneous clutter are studied: the amplitude and spectral width in each range bin are drawn from spatially invariant independent gamma distributed parent populations; clutter edges, in which the range profiles of the amplitude and/or spectrum exhibit a step change; and homogeneous clutter in which some range bins are corrupted by point-clutter sources or extraneous targets with significantly different amplitude and spectral characteristics. The use of prefilter MTI is investigated as a means of reducing losses in improvement factor (IF) in some classes of clutter nonhomogeneity. It is shown that adaptive filters suffer significant reductions in IF in nonhomogeneous clutter environments. These losses are generally greater than the benefit afforded by ideal adaptive processors over other conventional Doppler processors. The mean IF loss is an optimistic performance measure in adaptive Doppler processors.

EFFECT OF NON GAUSSIAN WEATHER CLUTTER ON PERFORMANCE OF CLUTTER-SUPPRESSION FILTERS

In this paper it is investigated the performance of clutter suppression filters in non Gaussian clutter. The model chosen is Log-normal clutter and the filters considered are the optimum MTI filter and the linear prediction error filter. The performance of both types of filters in Gaussian clutter as expressed by the improvement factor (I.F) is computed for different number of pulses N and different values of correlation coefficient . The results show that the optimal improvement factor is degraded for input non Gaussian clutter. While this situation can be improved if the complex weights are adjusted in correspondence with the covariance matrix of the Log-normal clutter. Comparison of performance of the two types ,of filters for different values of parameters is illust-rated quantitavely in graphical form.