scholarly journals Impact of Polarization Distortions on Geometrical Structure Retrieval of Moving Man-Made Targets in ISAR Images

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 373 ◽  
Author(s):  
Qiaoling Liu ◽  
Chen Pang ◽  
Yongzhen Li ◽  
Xuesong Wang
Keyword(s):  
Geometrical Structure ◽  
Phase Error ◽  
Real Data ◽  
Decomposition Methods ◽  
Scattering Matrix ◽  
Radar System ◽  
Synthetic Aperture ◽  
Residual Phase ◽  
Aerial Vehicle ◽  
The Impact

In this paper, the impact of several polarization distortions on geometrical structure retrieval of man-made targets in inverse synthetic aperture radar (ISAR) images is assessed. For polarimetric ISAR, the polarization distortions are usually composed of crosstalk and channel imbalance. For non-stationary targets, a residual phase error exists between two columns of the scattering matrix in the alternate transmission and simultaneous reception (ATSR) mode, especially when the radar frequency is high. These two kinds of distortions have varying influences on different targets, as well as different decomposition methods. Some theoretical derivations and numerical analyses are given to reveal the impact of these distortions on Cameron decomposition. To evaluate the impact of these distortions on geometrical structure retrieval, we use the numerical results and real data of an unmanned aerial vehicle (UAV) called Frontier to perform error analysis of both the decomposition and the retrieved size of the designated structures of the UAV. Some numerical evaluations were conducted from the perspective of system design, and these results can be useful for the practical radar system.

scholarly journals Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

2019 ◽  
Vol 11 (3) ◽  
pp. 340 ◽  
Author(s):  
Guanyong Wang ◽  
Man Zhang ◽  
Yan Huang ◽  
Lei Zhang ◽  
Fengfei Wang
Keyword(s):  
Cross Correlation ◽  
Phase Error ◽  
Synthetic Aperture ◽  
Two Dimensional ◽  
Spatial Variance ◽  
Phase Errors ◽  
Residual Phase ◽  
Aerial Vehicle ◽  
Sar Imagery ◽  
The One

Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively.

scholarly journals COMPARISON OF DECOMPOSITION METHODS OVER AGRICULTURAL FIELDS USING THE UAVSAR POLARIMETRIC SYNTHETIC APERTURE RADAR

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 649-653
Author(s):  
E. Kiana ◽  
S. Homayouni ◽  
M. A. Sharifi ◽  
M. R. Farid-Rohani
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Decomposition Methods ◽  
Synthetic Aperture ◽  
Agricultural Fields ◽  
Model Based ◽  
Component Decomposition ◽  
Validation Experiment ◽  
Aerial Vehicle ◽  
Aperture Radar

Abstract. This paper investigates and compares the potential of five model-based polarimetric decompositions, namely those developed by Eigenvector-based decomposition (Van Zyl), Model-based decomposition (Freeman-Durden three-component decomposition and Yamaguchi four-component decomposition), An & Yang3 and An & Yang4 for crop biomass detection over agricultural fields covered by various crops. The time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the ground truth of soil and vegetation characteristics collected during the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012 (SMAPVEX12) were used to compare the five decomposition methods with related to the scattering mechanisms and the biomass retrieval performances. The results show that the performance of each decomposition method for biomass retrieval depends on the crop types and the crop phenological stages. Finally, an overall biomass underestimation was observed from the five decompositions, and the highest regression value of 99% was obtained from Freeman decomposition as a result of the enhanced volume scattering. Indeed, Freeman-Durden model provided the best results.

scholarly journals Radar and UAV: georeferencing and airspace surveillance system

2018 ◽  
Vol 11 (2) ◽  
pp. 190-199
Author(s):  
Jonnathan Navarro Roa ◽  
Félix Parca Acevedo ◽  
Giovani Mancilla Gaona
Keyword(s):  
Surveillance System ◽  
Electromagnetic Waves ◽  
Radar System ◽  
Synthetic Aperture ◽  
Electromagnetic Signal ◽  
Radio Detection ◽  
Aerial Vehicle ◽  
The Doppler Effect ◽  
The Waves ◽  
Support Research

A ¨RADAR¨ system, from the English Acronym ¨Radio Detention and Ranging¨. Better known as ¨Radio Detection and Location¨ RDL.  It is a system for detecting and positioning objects through shipping and reflection of electromagnetic waves in terms of the time of issue and the return of the waves and their speed (calculated by the Doppler effect of the electromagnetic signal received).  Meanwhile, an autonomous unmanned vehicle, also known as Unmanned Aerial Vehicle, UAV, which is defined as an aircraft which does not carry a human operator, and uses aerodynamic forces to provide airlift by itself. Therefore, it can fly autonomously or it can be operated by remote control.  This article focuses on the review of experiences where these two technologies are integrated, particularly UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). This is done in order to support research concerning the combination thereof to be applied in georeferencing humanitarian missions or military missions that require monitoring of airspace. 

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Said Gounane ◽  
Yassir Barkouch ◽  
Abdelghafour Atlas ◽  
Mostafa Bendahmane ◽  
Fahd Karami ◽  
...  
Keyword(s):  
Mathematical Theory ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Disease Spreading ◽  
The Government ◽  
The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

Energy absorbed ability and damage analysis for honeycomb sandwich material of bio-inspired micro aerial vehicle under low velocity impact

2020 ◽  
pp. 095440622097542
Author(s):  
Jianxun Du ◽  
Peng Hao ◽  
Mabao Liu ◽  
Rui Xue ◽  
Lin’an Li
Keyword(s):  
Honeycomb Structure ◽  
Low Velocity Impact ◽  
Parameter Study ◽  
Low Velocity ◽  
Micro Aerial Vehicle ◽  
Thin Walled Structures ◽  
Wide Range ◽  
Aerial Vehicle ◽  
Thin Walled ◽  
The Impact

Because of the advantages of light weight, small size, and good maneuverability, the bio-inspired micro aerial vehicle has a wide range of application prospects and development potential in military and civil areas, and has become one of the research hotspots in the future aviation field. The beetle’s elytra possess high strength and provide the protection of the abdomen while being functional to guarantee its flight performance. In this study, the internal microstructure of beetle’s elytra was observed by scanning electron microscope (SEM), and a variety of bionic thin-walled structures were proposed and modelled. The energy absorption characteristics and protective performance of different configurations of thin-walled structures with hollow columns under impact loading was analyzed by finite element method. The parameter study was carried out to show the influence of the velocity of impactor, the impact angle of the impactor and the wall thickness of honeycomb structure. This study provides an important inspiration for the design of the protective structure of the micro aerial vehicle.

An improved method to estimate Q based on the logarithmic spectrum of moving peak points

2019 ◽  
Vol 7 (2) ◽  
pp. T255-T263 ◽  
Author(s):  
Yanli Liu ◽  
Zhenchun Li ◽  
Guoquan Yang ◽  
Qiang Liu
Keyword(s):  
Seismic Waves ◽  
Wave Attenuation ◽  
Real Data ◽  
Peak Frequency ◽  
Seismic Reflection Data ◽  
The Real ◽  
Time Frequency ◽  
Reflection Data ◽  
Text Filtering ◽  
The Impact

The quality factor ([Formula: see text]) is an important parameter for measuring the attenuation of seismic waves. Reliable [Formula: see text] estimation and stable inverse [Formula: see text] filtering are expected to improve the resolution of seismic data and deep-layer energy. Many methods of estimating [Formula: see text] are based on an individual wavelet. However, it is difficult to extract the individual wavelet precisely from seismic reflection data. To avoid this problem, we have developed a method of directly estimating [Formula: see text] from reflection data. The core of the methodology is selecting the peak-frequency points to linear fit their logarithmic spectrum and time-frequency product. Then, we calculated [Formula: see text] according to the relationship between [Formula: see text] and the optimized slope. First, to get the peak frequency points at different times, we use the generalized S transform to produce the 2D high-precision time-frequency spectrum. According to the seismic wave attenuation mechanism, the logarithmic spectrum attenuates linearly with the product of frequency and time. Thus, the second step of the method is transforming a 2D spectrum into 1D by variable substitution. In the process of transformation, we only selected the peak frequency points to participate in the fitting process, which can reduce the impact of the interference on the spectrum. Third, we obtain the optimized slope by least-squares fitting. To demonstrate the reliability of our method, we applied it to a constant [Formula: see text] model and the real data of a work area. For the real data, we calculated the [Formula: see text] curve of the seismic trace near a well and we get the high-resolution section by using stable inverse [Formula: see text] filtering. The model and real data indicate that our method is effective and reliable for estimating the [Formula: see text] value.

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