Global Monitoring and Characterization of Infrasound Signatures by Large Fireballs

2020 ◽  
Author(s):  
Christoph Pilger ◽  
Peter Gaebler ◽  
Patrick Hupe ◽  
Theresa Ott ◽  
Esther Drolshagen
Keyword(s):  
Correlation Method ◽  
Reference Database ◽  
Event Analysis ◽  
Source Characterization ◽  
Localization Accuracy ◽  
Propagation Modeling ◽  
Atmospheric Propagation ◽  
Observation Methods ◽  
Entry Behavior ◽  
Detection And Localization

<p>Large meteoroids can be registered in infrasound recordings during their entry into the Earth’s atmosphere. A comprehensive study of 10 large fireball events of the years 2018 and 2019 highlights their detection and characterization using global infrasound arrays of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The study focuses on the observation and event analysis of the fireballs to estimate their respective location, yield, trajectory, and entry behavior. Signal characteristics are derived by applying the Progressive Multi-Channel Correlation method as an array technique. The comparison of the events with a NASA reference database as well as the application of atmospheric propagation modeling allows to draw conclusions about infrasound-based detection capability, localization accuracy, yield estimation, and source characterization. The infrasound technique provides a time- and location-independent remote monitoring opportunity of impacting near-Earth objects (NEOs), either independent or complementary to other fireball observation methods. Additionally, insights about the detection and localization capability of IMS infrasound stations can be gained from using large fireballs as reference events, being of importance for the continuous monitoring and verification of atmospheric explosions in a CTBT context.</p>

scholarly journals Global Monitoring and Characterization of Infrasound Signatures by Large Fireballs

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 83 ◽  
Author(s):  
Christoph Pilger ◽  
Peter Gaebler ◽  
Patrick Hupe ◽  
Theresa Ott ◽  
Esther Drolshagen
Keyword(s):  
Correlation Method ◽  
Reference Database ◽  
Event Analysis ◽  
Source Characterization ◽  
Localization Accuracy ◽  
Propagation Modeling ◽  
Atmospheric Propagation ◽  
Observation Methods ◽  
Entry Behavior ◽  
Detection And Localization

Large meteoroids can be registered in infrasound recordings during their entry into the Earth’s atmosphere. A comprehensive study of 10 large fireball events of the years 2018 and 2019 highlights their detection and characterization using global infrasound arrays of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The study focuses on the observation and event analysis of the fireballs to estimate their respective location, yield, trajectory, and entry behavior. Signal characteristics are derived by applying the Progressive Multi-Channel Correlation method as an array technique. The comparison of the events with a NASA reference database as well as the application of atmospheric propagation modeling allows to draw conclusions about infrasound-based detection capability, localization accuracy, yield estimation, and source characterization. The infrasound technique provides a time- and location-independent remote monitoring opportunity of impacting near-Earth objects (NEOs), either independent or complementary to other fireball observation methods. Additionally, insights about the detection and localization capability of IMS infrasound stations can be gained from using large fireballs as reference events, being of importance for the continuous monitoring and verification of atmospheric explosions in a CTBT context.

scholarly journals Weakly Supervised Learning for Object Localization Based on an Attention Mechanism

2021 ◽  
Vol 11 (22) ◽  
pp. 10953
Author(s):  
Nojin Park ◽  
Hanseok Ko
Keyword(s):  
Deep Learning ◽  
Experimental Results ◽  
Object Localization ◽  
Localization Accuracy ◽  
Large Size ◽  
Bounding Box ◽  
Improved Performance ◽  
Weakly Supervised ◽  
Detection And Localization ◽  
Activation Map

Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results show that the proposed method achieves the best performance, with 75.21% Top-1 classification accuracy and 55.15% Top-1 localization accuracy on the CUB-200-2011 dataset.

3‐D airgun source characterization and propagation modeling

2006 ◽  
Author(s):  
Arslan M. Tashmukhambetov ◽  
Natalia A. Sidorovskaia ◽  
George E. Ioup ◽  
Juliette W. Ioup ◽  
Joal J. Newcomb ◽  
...  
Keyword(s):  
Source Characterization ◽  
Propagation Modeling

scholarly journals Saliency-Based Diver Target Detection and Localization Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjiang Zhu ◽  
Siquan Yu ◽  
Lei Gao ◽  
Zhi Han ◽  
Yandong Tang
Keyword(s):  
Saliency Detection ◽  
Region Of Interest ◽  
Saliency Map ◽  
Shadow Region ◽  
Localization Accuracy ◽  
Acoustic Shadow ◽  
Sonar Image ◽  
Sonar Images ◽  
Detection And Localization

Diver target automatic detection is indispensable for underwater defense systems, such as the unmanned harbor surveillance system. It is a very challenging task due to various poses and intensity features of diver target. In addition, the background noise in sonar images is complex, which also makes the task more difficult. In this paper, we propose a diver detection method based on saliency detection for sonar images. On the basis of studying the characteristics of diver sonar images, we first decompose the original sonar image and perform median filtering on it, which can significantly improve the quality of the sonar image saliency map. We employ saliency detection technique based on frequency analysis to segment the acoustic highlight region from its surroundings. This segmentation region roughly locates the diver target and generates a region of interest (ROI). We then extract the acoustic shadow region in ROI, which contributes to furtherly improve the localization accuracy. Finally, we merge the segmented highlight region and the extracted acoustic shadow region and compute the minimum outer rectangle of the merged region. Experimental results validate that the proposed method can well detect and locate the diver target, and it can also satisfy the demands of real-time application, and there is almost no false alarm in this method.

scholarly journals DETECTION AND LOCALIZATION OF ELECTRIC DISCHARGES IN INDOOR SUBSTATION FOR FAULT MONITORING

2019 ◽  
Vol 1 (2) ◽  
pp. 94-96
Author(s):  
Lorothy Singkang
Keyword(s):  
Power System ◽  
Array Antenna ◽  
Electric Discharges ◽  
Localization Accuracy ◽  
Fault Monitoring ◽  
Monitoring Process ◽  
Power Substation ◽  
Insulation Degradation ◽  
Element Array ◽  
Detection And Localization

Power Substation is the most important unit in the power system, therefore, the monitoring process must be carried out effectively to detect the operation status of the equipment, and the maintenance is necessary for safe operation. Substation faulty such as Dielectric breakdowns, originating from the insulation degradation is still a major issue in the power system (1–3).  Many methods and techniques with intelligence approaches have been developed to provide a better way of fault detection in a substation. However, not many are willing to adopt those techniques by reasoning the high cost of installation and more sensors required to improve localization accuracy (4). Therefore, to reduce cost and increase the speed of detection, this paper presents a 2-element array antenna to perform as a sensor to detect and localize the electric discharges (ED) produced by abnormal radiated electromagnetic activities in substation based on the direction of arriving angle (DOA) received by elements in the array antenna. The radiation patterns obtained were then visualized using software of signal processing based on the normalized Array Factor (AFN). This sensor has shown its efficiency in eliminating the interferer signals at random DOA of  and maximizing the desired signal at DOA of 45Ëš; the identified angle direction from the substation power apparatus.  This sensor has the ability to be steered isotopically and terminate or maximize signals which differ by  or 0.0873 radians of DOAs, simultaneously. Having these abilities allowed this sensor to be a cohesive unit in detecting and localizing the abnormal radiated electromagnetic activities in substation based on the identified DOA thus, make it as a promising preventive approach for substation breakdown and improve the performance in Substation Fault Monitoring.

scholarly journals Determination of Detection Probability and Localization Accuracy for a Guided Wave-Based Structural Health Monitoring System on a Composite Structure

2021 ◽  
Vol 2 (4) ◽  
pp. 996-1008
Author(s):  
Ahmed Bayoumi ◽  
Tobias Minten ◽  
Inka Mueller
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Detection Probability ◽  
Guided Wave ◽  
Detection Sensitivity ◽  
Localization Accuracy ◽  
Structural Health ◽  
Damage Location ◽  
Detection And Localization

The capabilities of detection and localization of damage in a structure, using a guided wave-based structural health monitoring (GWSHM) system, depend on the damage location and the chosen sensor array setup. This paper presents a novel approach to assess the reliability of an SHM system enabling to quantify localization accuracy. A two-step technique is developed to combine multiple paths to generate one probability of detection (POD) curve that provides information regarding the detection capability of an SHM system at a defined damage position. Moreover, a new method is presented to analyze localization accuracy. Established probability-based diagnostic imaging using a signal correlation algorithm is used to determine the damage location. The resultant output of the localization accuracy analysis is the smallest damage size at which a defined accuracy level can be reached at a determined location. The proposed methods for determination of detection probability and localization accuracy are applied to a plate-like CFRP structure with an omega stringer with artificial damage of different sizes at different locations. The results show that the location of the damage influences the sensitivity of detection and localization accuracy for the used detection and localization methods. Localization accuracy is enhanced as it becomes closer to the array’s center, but its detection sensitivity deteriorates.

scholarly journals A Case Study of Whistle Detection and Localization for Humpback Dolphins in Taiwan

2021 ◽  
Vol 9 (7) ◽  
pp. 725
Author(s):  
Ching-Tang Hung ◽  
Wei-Yen Chu ◽  
Wei-Lun Li ◽  
Yen-Hsiang Huang ◽  
Wei-Chun Hu ◽  
...  
Keyword(s):  
Sound Source ◽  
Marine Mammal ◽  
Offshore Wind ◽  
Real Field ◽  
Ecological Disturbance ◽  
Localization Accuracy ◽  
Passive Acoustic ◽  
Detection And Localization ◽  
Moving Path ◽  
Detection Processing

In recent years, Taiwan’s government has focused on policies regarding offshore wind farming near the Indo-Pacific humpback dolphin habitat, where marine mammal observation is a critical consideration. The present research developed an algorithm called National Taiwan University Passive Acoustic Monitoring (NTU_PAM) to assist marine mammal observers (MMOs). The algorithm performs whistle detection processing and whistle localization. Whistle detection processing is based on image processing and whistle feature extraction; whistle localization is based on the time difference of arrival (TDOA) method. To test the whistle detection performance, we used the same data to compare NTU_PAM and the widely used software PAMGuard. To test whistle localization, we designed a real field experiment where a sound source projected simulated whistles, which were then recorded by several hydrophone stations. The data were analyzed to locate the moving path of the source. The results show that localization accuracy was higher when the sound source position was in the detection region composed of hydrophone stations. This paper provides a method for MMOs to conveniently observe the migration path and population dynamics of cetaceans without ecological disturbance.

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