scholarly journals Gyroscope-Based Video Stabilization for Electro-Optical Long-Range Surveillance Systems

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6219
Author(s):  
Petar D. Milanović ◽  
Ilija V. Popadić ◽  
Branko D. Kovačević
Keyword(s):  
Long Range ◽  
Weather Conditions ◽  
Optical Systems ◽  
Video Stabilization ◽  
Electromagnetic Spectrum ◽  
Surveillance Systems ◽  
Feature Based ◽  
Gyro Bias ◽  
Bias Instability ◽  
Estimation Quality

Video stabilization is essential for long-range electro-optical systems, especially in situations when the field of view is narrow, since the system shake may produce highly deteriorating effects. It is important that the stabilization works for different camera types, i.e., different parts of the electromagnetic spectrum independently of the weather conditions and any form of image distortion. In this paper, we propose a method for real-time video stabilization that uses only gyroscope measurements, analyze its performance, and implement and validate it on a real-world professional electro-optical system developed at Vlatacom Institute. Camera movements are modeled with 3D rotations obtained by integration of MEMS gyroscope measurements. The 3D orientation estimation quality depends on the gyroscope characteristics; we provide a detailed discussion on the criteria for gyroscope selection in terms of the sensitivity, measurement noise, and drift stability. Furthermore, we propose a method for improving the unwanted motion estimation quality using interpolation in the quaternion domain. We also propose practical solutions for eliminating disturbances originating from gyro bias instability and noise. In order to evaluate the quality of our solution, we compared the performance of our implementation with two feature-based digital stabilization methods. The general advantage of the proposed methods is its drastically lower computational complexity; hence, it can be implemented for a low price independent of the used electro-optical sensor system.

scholarly journals Thermal Imager Range: Predictions, Expectations, and Reality

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3313 ◽  
Author(s):  
Dragana Perić ◽  
Branko Livada ◽  
Miroslav Perić ◽  
Saša Vujić
Keyword(s):  
Long Range ◽  
Imaging System ◽  
Weather Conditions ◽  
Theoretical Models ◽  
Field Conditions ◽  
Design Parameters ◽  
Surveillance Systems ◽  
Thermal Imager ◽  
Adverse Weather ◽  
Thermal Imagers

Imaging system range defines the maximal distance at which a selected object can be seen and perceived following surveillance task perception criteria. Thermal imagers play a key role in long-range surveillance systems due to the ability to form images during the day or night and in adverse weather conditions. The thermal imager range depends on imager design parameters, scene and transmission path properties. Imager range prediction is supported by theoretical models that provide the ability to check range performance, compare range performances for different systems, extend range prediction in field conditions, and support laboratory measurements related to range. A condensed review of the theoretical model’s genesis and capabilities is presented. We applied model-based performance calculation for several thermal imagers used in our long-range surveillance systems and compared the results with laboratory performance measurement results with the intention of providing the range prediction in selected field conditions. The key objective of the paper is to provide users with reliable data regarding expectations during a field mission.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

scholarly journals Possibilities of long-range forecast of weather conditions over the Antarctic peninsula

2009 ◽  
pp. 428-440
Author(s):  
V. F. Martazinova ◽  
◽  
V.S. Maderich ◽  
V. Y. Tymofeyev ◽  
E. K. Ivanova ◽  
...  
Keyword(s):  
Long Range ◽  
Antarctic Peninsula ◽  
Weather Conditions ◽  
The Antarctic

Research on long-range laser active imaging system applied in adverse weather conditions

2013 ◽  
Author(s):  
Zhi-gang Gai ◽  
Meng-de Liu ◽  
Li Yang ◽  
V. V. Kabanov ◽  
Lei Shi ◽  
...  
Keyword(s):  
Long Range ◽  
Imaging System ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Active Imaging

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

scholarly journals Rain attenuation prediction model for satellite communications based on the Météo-France ensemble prediction system PEARP

2018 ◽  
Vol 18 (12) ◽  
pp. 3327-3341 ◽  
Author(s):  
Isabelle Dahman ◽  
Philippe Arbogast ◽  
Nicolas Jeannin ◽  
Bouchra Benammar
Keyword(s):  
Communication Systems ◽  
Weather Forecasting ◽  
Weather Conditions ◽  
Satellite Communications ◽  
General Idea ◽  
Ensemble Prediction ◽  
Second Phase ◽  
Electromagnetic Spectrum ◽  
Adverse Weather ◽  
Mitigation Techniques

Abstract. This paper presents an example of the usage of ensemble weather forecasting for the control of satellite-based communication systems. Satellite communication systems become increasingly sensitive to weather conditions as their operating frequency increases to avoid electromagnetic spectrum congestion and enhance their capacity. In the microwave domain, electromagnetic waves that are conveying information are attenuated between the satellite and Earth terminals in the presence of hydrometeors (mostly rain drops and more marginally cloud droplets). To maintain a reasonable level of service availability, even with adverse weather conditions considering the scarcity of amplification power in spacecraft, fade mitigation techniques have been developed. The general idea behind those fade mitigation techniques is to reroute, change the characteristics or reschedule the transmission in the case of too-significant propagation impairments. For some systems, a scheduling on how to use those mechanisms some hours in advance is required, making assumptions on the future weather conditions affecting the link. To this aim the use of weather forecast data to control the attenuation compensation mechanisms seems of particular interest to maximize the performances of the communication links and hence of the associated economic value. A model to forecast the attenuation on the link based on forecasted rainfall amounts from deterministic or ensemble weather forecasting is presented and validated. In a second phase, the model's application to a simplified telecommunication system allows us to demonstrate the valuable contribution of weather forecasting in the system's availability optimization or in the system's throughput optimization. The benefit of using ensemble forecasts rather than deterministic ones is demonstrated as well.

scholarly journals Investigations of Free Space Optical Communications Under Real-World Atmospheric Conditions

2020 ◽  
Vol 116 (1) ◽  
pp. 475-490 ◽  
Author(s):  
Tobias Siegel ◽  
Shun-Ping Chen
Keyword(s):  
Optical Communications ◽  
Free Space ◽  
Communication Systems ◽  
New Technologies ◽  
Weather Conditions ◽  
Optical Systems ◽  
Limiting Factors ◽  
Atmospheric Conditions ◽  
Free Space Optical ◽  
Atmospheric Channel

AbstractDue to the increasing demand for higher bandwidth in modern communication systems, conventional networks are continuously expanded with new technologies to improve coverage. Free space optical communications (FSOC) shows some significant advantages concerning system setup time in comparison with the classical fiber optical systems on one hand, substantial spectral bandwidth and performances in comparison with the wireless systems under certain conditions on the other hand. This makes this technology not only a reasonable extension for metropolitan area networks but also provides the capability to set up a network after an outage in case of natural disaster quickly. But transmitting data by using FSOC involves some limiting factors that have to be considered prior to each installation. Since the atmospheric channel is not static, the influence of changing weather conditions or industrial smog have a significant impact on the available bitrate. A simulation platform is developed and presented in this paper for investigation of FSOC considering these circumstances. Regarding the atmospheric channel, turbulence, distance-dependent beam divergence, and applied modulation schemes, a general overview of the capabilities is presented and discussed. The insight of this paper should help to make a decision under which preconditions either the FSOC provides a meaningful application possibility, or the limiting factors become too crucial and other technologies must be considered.

scholarly journals Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2799
Author(s):  
Stanisław Hożyń ◽  
Jacek Zalewski
Keyword(s):  
Image Processing ◽  
Adaptive Filtering ◽  
Weather Conditions ◽  
Optical Systems ◽  
Probabilistic Clustering ◽  
Autonomous Surface Vehicles ◽  
Autonomous Surface Vehicle ◽  
Clustering Model ◽  
Surrounding Landscape ◽  
Traditional Image

Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.

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