scholarly journals An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 274 ◽  
Author(s):  
Shengying Yang ◽  
Huibin Qin ◽  
Xiaolin Liang ◽  
Thomas Gulliver
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Fingerprint Analysis ◽  
Detection Algorithms ◽  
Rf Signals ◽  
Aerial Vehicle ◽  
Value Decomposition

Unmanned aerial vehicles (UAVs) are now readily available worldwide and users can easily fly them remotely using smart controllers. This has created the problem of keeping unauthorized UAVs away from private or sensitive areas where they can be a personal or public threat. This paper proposes an improved radio frequency (RF)-based method to detect UAVs. The clutter (interference) is eliminated using a background filtering method. Then singular value decomposition (SVD) and average filtering are used to reduce the noise and improve the signal to noise ratio (SNR). Spectrum accumulation (SA) and statistical fingerprint analysis (SFA) are employed to provide two frequency estimates. These estimates are used to determine if a UAV is present in the detection environment. The data size is reduced using a region of interest (ROI), and this improves the system efficiency and improves azimuth estimation accuracy. Detection results are obtained using real UAV RF signals obtained experimentally which show that the proposed method is more effective than other well-known detection algorithms. The recognition rate with this method is close to 100% within a distance of 2.4 km and greater than 90% within a distance of 3 km. Further, multiple UAVs can be detected accurately using the proposed method.

scholarly journals Design and Optimization for UAV-Enabled Two-Way Relaying System with SWIPT

2020 ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two userequipment exchange information with the help of UAV relay and harvest energythrough power splitting (PS) scheme. Under the transmission power constraintsat UEs and UAV relay, a non-convex intractable optimization problem isformulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factors optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of two proposed schemes.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

2018 ◽  
Vol 11 (6) ◽  
pp. 2455-2474 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

scholarly journals Parameter estimation and signal detection algorithm based on adaptive capture in non-cooperative communication

2021 ◽  
Vol 336 ◽  
pp. 04002
Author(s):  
Zilong He ◽  
Peng Sun ◽  
Kexian Gong ◽  
Hua Jiang
Keyword(s):  
Parameter Estimation ◽  
Signal Detection ◽  
Cooperative Communication ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Signal Spectrum

Aiming at the problem that the frequency offset in the non-cooperative communication system causes the received signal spectrum to shift, which exceeds the passband of the matched filter and affects the subsequent demodulation, a parameter estimation and signal detection algorithm based on adaptive capture is proposed by this paper, which is more convenient for hardware implementation and consumes less resources. The algorithm is divided into three parts. Firstly, use the correlation value between the signal and the preamble sequence as the basis for frequency capture. Secondly, the frequency is accurately estimated based on the interpolation algorithm. Finally, the phase-locked loop structure is used to track the frequency according to the characteristics of the frequency gradually changing and the signal frequency offset is eliminated in the Digital Down Converter stage. It provides necessary conditions for accurate signal detection and phase estimation. The simulation results show that the algorithm has high estimation accuracy, wide esti-mation range and low complexity. It can also achieve better estimation accuracy and detection performance under low signal-to-noise ratio.

scholarly journals An algorithm for detecting the Chesapeake Bay breeze from mesoscale NWP model output

2021 ◽  
Keyword(s):  
Chesapeake Bay ◽  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Model Output ◽  
Detection Algorithms ◽  
Bay Breeze ◽  
Nwp Model

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) model and on analyses and forecasts from the High Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. Additionally, by defining the onshore wind directions based on model land-use data, not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

scholarly journals Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1223 ◽  
Author(s):  
Xiaoyan Wei ◽  
Yirong Wu ◽  
Fangmin Dong ◽  
Jun Zhang ◽  
Shuifa Sun
Keyword(s):  
Edge Detection ◽  
Feature Fusion ◽  
Interpolation Method ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Image Manipulation ◽  
Bilinear Interpolation ◽  
Detection Algorithms ◽  
Wide Availability ◽  
Manipulation Detection

Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.

scholarly journals Design and Optimization for Uav-enabled Two-way Relaying System With Swipt

2020 ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two user equipments (UEs) exchange information with the help of UAV relay and harvest energy through power splitting (PS) scheme. Under the transmission power constraints at UEs and UAV relay, a non-convex intractable optimization problem is formulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factors optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of two proposed schemes.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project Goals and Experimental Design

2018 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

scholarly journals Hue–saturation–intensity and texture feature-based cloud detection algorithm for unmanned aerial vehicle images

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142090353
Author(s):  
Wang Yi ◽  
Zhang Jing ◽  
Gao Shuang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Identification ◽  
False Positive Rate ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Detection Algorithms ◽  
Saturation Intensity ◽  
Positive Rate ◽  
Aerial Vehicle

There are a large number of cloud-covered areas in most unmanned aerial vehicle images and lead to the loss of information in the image and affect image post procession such as image fusion and target identification. Finding the cloud-occluded area in an image is a key step in image processing. Based on the differences of color and texture characteristics between cloud and ground, a cloud detection algorithm for the unmanned aerial vehicle images is proposed. Simulation results show that the proposed algorithm is better than the classical cloud detection algorithms in accuracy rate, false-positive rate, and kappa coefficient.

scholarly journals A Robust Detection Algorithm for Infrared Maritime Small and Dim Targets

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1237 ◽  
Author(s):  
Yuwei Lu ◽  
Lili Dong ◽  
Tong Zhang ◽  
Wenhai Xu
Keyword(s):  
Target Detection ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Texture Features ◽  
Detection Algorithm ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Region Selection ◽  
The Difference ◽  
Small Targets

Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments.

scholarly journals Design and optimization for UAV-enabled two-way relaying system with SWIPT

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two user equipment exchange information with the help of UAV relay and harvest energy through power splitting (PS) scheme. Under the transmission power constraints at UEs and UAV relay, a non-convex intractable optimization problem is formulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factor optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of the two proposed schemes.

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