An Improved Phase-Derived Range Method Based on High-Order Multi-Frame Track-Before-Detect for Warhead Detection

It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be separated by its micro-Doppler features introduced by the precession and nutation. As is well known, the accuracy of the phase-derived range method, to extract micro-Doppler curves, can reach sub-wavelength. However, it suffers from an inefficiency of energy integration and high computational costs. In this paper, a novel phase-derived range method, using high-order multi-frame track-before-detect is proposed for micro-Doppler curve extraction under a low signal-to-noise ratio (SNR). First, the sinusoidal micro-Doppler range sequence is treated as the state, and the dynamic model is described as a Markov chain to obtain the envelopes and then the ambiguous phases. Instead of processing the whole frames, the proposed method only processes the latest frame at an arbitrary given time, which reduces the computational costs. Then, the correlation of all pairs of adjacent pulses is calculated along the slow time dimension to find the number of cells that the point scatterer crosses, which can be further used in phase unwrapping. Finally, the phase-derived range method is employed to get the micro-Doppler curves. Simulation results show that the proposed method is capable of extracting the micro-Doppler curves with sub-wavelength accuracy, even if SNR = −15 dB, with a lower computational cost.

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A Study of Clustering for the Enhancement of Image Resolution in Dynamic Photon Emission

ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2019p0068 ◽

2019 ◽

Author(s):

S. Chef ◽

C. T. Chua ◽

C. L. Gan

Keyword(s):

Signal To Noise Ratio ◽

Photon Emission ◽

Optical Signal ◽

Image Resolution ◽

Time Dimension ◽

Processing Scheme ◽

3D Space ◽

Initial Acquisition ◽

Resolution Limits ◽

Emission Microscopy

Abstract Limited spatial resolution and low signal to noise ratio are some of the main challenges in optical signal observation, especially for photon emission microscopy. As dynamic emission signals are generated in a 3D space, the use of the time dimension in addition to space enables a better localization of switching events. It can actually be used to infer information with a precision above the resolution limits of the acquired signals. Taking advantage of this property, we report on a post-acquisition processing scheme to generate emission images with a better image resolution than the initial acquisition.

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Performance of Adaptive Unstructured Mesh Modelling in Idealized Advection Cases over Steep Terrains

Atmosphere ◽

10.3390/atmos9110444 ◽

2018 ◽

Vol 9 (11) ◽

pp. 444 ◽

Cited By ~ 2

Author(s):

Jinxi Li ◽

Jie Zheng ◽

Jiang Zhu ◽

Fangxin Fang ◽

Christopher. Pain ◽

...

Keyword(s):

Unstructured Mesh ◽

Computational Cost ◽

Adaptive Mesh ◽

Galerkin Finite Element Method ◽

Adaptive Meshes ◽

Galerkin Finite Element ◽

Computational Costs ◽

Cut Cell ◽

Discontinuous Galerkin Finite Element ◽

Terrain Following

Advection errors are common in basic terrain-following (TF) coordinates. Numerous methods, including the hybrid TF coordinate and smoothing vertical layers, have been proposed to reduce the advection errors. Advection errors are affected by the directions of velocity fields and the complexity of the terrain. In this study, an unstructured adaptive mesh together with the discontinuous Galerkin finite element method is employed to reduce advection errors over steep terrains. To test the capability of adaptive meshes, five two-dimensional (2D) idealized tests are conducted. Then, the results of adaptive meshes are compared with those of cut-cell and TF meshes. The results show that using adaptive meshes reduces the advection errors by one to two orders of magnitude compared to the cut-cell and TF meshes regardless of variations in velocity directions or terrain complexity. Furthermore, adaptive meshes can reduce the advection errors when the tracer moves tangentially along the terrain surface and allows the terrain to be represented without incurring in severe dispersion. Finally, the computational cost is analyzed. To achieve a given tagging criterion level, the adaptive mesh requires fewer nodes, smaller minimum mesh sizes, less runtime and lower proportion between the node numbers used for resolving the tracer and each wavelength than cut-cell and TF meshes, thus reducing the computational costs.

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An Improved Acquisition Method for High-Order BOC-Modulated Signals Based on Fractal Geometry

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1185 ◽

2013 ◽

Vol 846-847 ◽

pp. 1185-1188 ◽

Cited By ~ 1

Author(s):

Hua Bing Wu ◽

Jun Liang Liu ◽

Yuan Zhang ◽

Yong Hui Hu

Keyword(s):

Fractal Geometry ◽

Output Signal ◽

Signal To Noise Ratio ◽

High Order ◽

Main Peak ◽

Signal To Noise ◽

Binary Offset Carrier ◽

Simulation Results ◽

Modulated Signals ◽

Acquisition Method

This paper proposes an improved acquisition method for high-order binary-offset-carrier (BOC) modulated signals based on fractal geometry. We introduced the principle of our acquisition method, and outlined its framework. We increase the main peak to side peaks ratio in the BOC autocorrelation function (ACF), with a simple fractal geometry transform. The proposed scheme is applicable to both generic high-order sine-and cosine-phased BOC-modulated signals. Simulation results show that the proposed method increases output signal to noise ratio (SNR).

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Wind Turbine Noise Reduction from Seismological Data

10.5194/egusphere-egu21-4907 ◽

2021 ◽

Author(s):

Janis Heuel ◽

Wolfgang Friederich

Keyword(s):

Signal To Noise Ratio ◽

Low Cost ◽

Computational Cost ◽

Noise Signal ◽

Noise Model ◽

Threshold Function ◽

High Signal ◽

Time Data ◽

Negative Effects ◽

Before And After

Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to negative effects on humans, WTs are often installed in areas with low population density. Because of low anthropogenic noise, these areas are also well suited for sites of seismological stations. As a consequence, WTs are often installed in the same areas as seismological stations. By comparing the noise in recorded data before and after installation of WTs, seismologists noticed a substantial worsening of station quality leading to conflicts between the operators of WTs and earthquake services.In this study, we compare different techniques to reduce or eliminate the disturbing signal from WTs at seismological stations. For this purpose, we selected a seismological station that shows a significant correlation between the power spectral density and the hourly windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic data processing. However, this approach is not effective when noise and signal have overlapping frequency bands which is the case for WT noise. As a first method, we applied the continuous wavelet transform (CWT) on our data to obtain a time-scale representation. From this representation, we estimated a noise threshold function (Langston & Mousavi, 2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we installed low cost seismometers at the surrounding WTs to find similar signals at each WT. From these similar signals, we obtain a noise model at the seismological station, which is used to estimate the threshold function. As a second method, we used a denoising autoencoder (DAE) that learns mapping functions to distinguish between noise and signal (Zhu et al., 2019).In our tests, the threshold function performs well when the event is visible in the raw or spectral filtered data, but it fails when WT noise dominates and the event is hidden. In these cases, the DAE removes the WT noise from the data. However, the DAE must be trained with typical noise samples and high signal-to-noise ratio events to distinguish between signal and interfering noise. Using the threshold function and pre-noise can be applied immediately on real-time data and has a low computational cost. Using a noise model from our prerecorded database at the seismological station does not improve the result and it is more time consuming to find similar ground velocity conditions at the surrounding WTs.

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Effective Filter Pruning Method Using Additional Downsampled Image for Global Pooling Applied CNN

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142154029x ◽

2021 ◽

pp. 2154029

Author(s):

Hyunseok Kim ◽

Bunyodbek Ibrokhimov ◽

Sanggil Kang

Keyword(s):

Evaluation Method ◽

Computational Cost ◽

Information Loss ◽

Deep Convolutional Neural Networks ◽

Embedded Devices ◽

Computational Costs ◽

Pruning Method ◽

Original Dataset ◽

High Resolution Images ◽

Deep Cnn

Deep Convolutional Neural Networks (CNNs) show remarkable performance in many areas. However, most of the applications require huge computational costs and massive memory, which are hard to obtain in devices with a relatively weak performance like embedded devices. To reduce the computational cost, and meantime, to preserve the performance of the trained deep CNN, we propose a new filter pruning method using an additional dataset derived by downsampling the original dataset. Our method takes advantage of the fact that information in high-resolution images is lost in the downsampling process. Each trained convolutional filter reacts differently to this information loss. Based on this, the importance of the filter is evaluated by comparing the gradient obtained from two different resolution images. We validate the superiority of our filter evaluation method using a VGG-16 model trained on CIFAR-10 and CUB-200-2011 datasets. The pruned network with our method shows an average of 2.66% higher accuracy in the latter dataset, compared to existing pruning methods when about 75% of the parameters are removed.

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A Robust Procedure to Implement Dynamic Stall Models Into Actuator Line Methods for the Simulation of Vertical-Axis Wind Turbines

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4051909 ◽

2021 ◽

Author(s):

Pier Francesco Melani ◽

Francesco Balduzzi ◽

Alessandro Bianchini

Keyword(s):

Flow Field ◽

Wind Turbines ◽

Signal To Noise Ratio ◽

Computational Cost ◽

Vertical Axis ◽

Dynamic Stall ◽

Lumped Parameter ◽

Low Pass ◽

Unsteady Rans ◽

Novel Method

Abstract The Actuator Line Method (ALM), combining a lumped-parameter representation of the rotating blades with the CFD resolution of the turbine flow field, stands out among the modern simulation methods for wind turbines as probably the most interesting compromise between accuracy and computational cost. Being however a method relying on tabulated coefficients for modeling the blade-flow interaction, the correct implementation of the sub-models to account for higher order aerodynamic effects is pivotal. Inter alia, the introduction of a dynamic stall model is extremely challenging: first, it is important to extrapolate a correct value of the angle of attack (AoA) from the solved flow field; second, the AoA history needed to calculate the rate of dynamic variation of the angle itself is characterized by a low signal-to-noise ratio, leading to severe numerical oscillations of the solution. The study introduces a robust procedure to improve the quality of the AoA signal extracted from an ALM simulation. It combines a novel method for sampling the inflow velocity from the numerical flow field with a low-pass filtering of the corresponding AoA signal based on Cubic Spline Smoothing. Such procedure has been implemented in the Actuator Line module developed by the authors for the commercial ANSYS® FLUENT® solver. To verify the reliability of the methodology, two-dimensional unsteady RANS simulations of a test 2-blade Darrieus H-rotor, for which high-fidelity experimental and numerical blade loading data were available, have been performed for a selected unstable operation point.

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High-Order Hybrid WCNS-CPR Scheme for Shock Capturing of Conservation Laws

International Journal of Aerospace Engineering ◽

10.1155/2020/8825445 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jia Guo ◽

Huajun Zhu ◽

Zhen-Guo Yan ◽

Lingyan Tang ◽

Songhe Song

Keyword(s):

Computational Cost ◽

High Order ◽

Shock Capturing ◽

High Order Accuracy ◽

Hybrid Technique ◽

Hybrid Scheme ◽

Order Accuracy ◽

Order Of Accuracy ◽

Reconstruction Scheme ◽

High Gradients

By introducing hybrid technique into high-order CPR (correction procedure via reconstruction) scheme, a novel hybrid WCNS-CPR scheme is developed for efficient supersonic simulations. Firstly, a shock detector based on nonlinear weights is used to identify grid cells with high gradients or discontinuities throughout the whole flow field. Then, WCNS (weighted compact nonlinear scheme) is adopted to capture shocks in these areas, while the smooth area is calculated by CPR. A strategy to treat the interfaces of the two schemes is developed, which maintains high-order accuracy. Convergent order of accuracy and shock-capturing ability are tested in several numerical experiments; the results of which show that this hybrid scheme achieves expected high-order accuracy and high resolution, is robust in shock capturing, and has less computational cost compared to the WCNS.

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Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Sensors ◽

10.3390/s18124115 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4115 ◽

Cited By ~ 3

Author(s):

Feng Lian ◽

Liming Hou ◽

Bo Wei ◽

Chongzhao Han

Keyword(s):

Target Tracking ◽

Large Scale ◽

Sequential Monte Carlo ◽

Signal To Noise Ratio ◽

Computational Cost ◽

Gaussian Mixture ◽

Descent Method ◽

Sensor Selection ◽

Multi Target Tracking ◽

Finite Set

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

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Efficient construction of linear models in materials modeling and applications to force constant expansions

npj Computational Materials ◽

10.1038/s41524-020-00404-5 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Erik Fransson ◽

Fredrik Eriksson ◽

Paul Erhart

Keyword(s):

Feature Selection ◽

Thermodynamic Properties ◽

Force Constant ◽

Linear Models ◽

Materials Science ◽

Computational Cost ◽

Ordinary Least Squares ◽

High Order ◽

Unit Cells ◽

Low Symmetry

Abstract Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science.

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A Novel 2-D Geometry Reconstruction Approach for Space Debris via Interpolation-Free Operation under Low SNR Conditions

Remote Sensing ◽

10.3390/rs12122059 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2059

Author(s):

Xi Luo ◽

Lixin Guo ◽

Dong Li ◽

Hongqing Liu ◽

Mengyi Qin

Keyword(s):

Signal To Noise Ratio ◽

Computational Cost ◽

Low Snr ◽

Phase Errors ◽

Isar Imaging ◽

Key Issues ◽

Imaging Mode ◽

Imaging Performance ◽

Low Computational Cost ◽

Spinning Targets

Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of SAR, it is well known that azimuth spatial invariant characteristics exist, and inspired by this, we propose a fast ISAR imaging approach for spinning targets. Our approach involves two steps. First, a precise analytic expression in the range-Doppler (RD) domain is produced using the principle of stationary phase (POSP). Second, a novel interpolation kernel function is designed to remove two-dimensional (2-D) spatial-variant phase errors, and the corresponding fast implementation steps that only require Fourier transform and multiplications are also presented. Finally, a well-focused ISAR image is obtained by compensating the azimuth high-order terms. Compared with current imaging methods, our approach avoids multi-dimensional search and interpolation operations and exploits the 2-D coherent integrated gain; the proposed method is of low computational cost and robustness in the low SNR condition. The effectiveness of the proposed approach is confirmed by numerically simulated experiments.

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