Prediction of Target Detection Probability Based on Air-to-Air Long-Range Scenarios in Anomalous Atmospheric Environments

Lim, Tae Heung; Choo, Hosung

doi:10.3390/rs13193943

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…In some cases, the signal loss may be stronger than predicted by this equation. The reason for this uncertainty is dissipation due to refraction (lensing) [37], which is not considered in this equation.…”

Section: Atmospheric Precipitation Losses and Weather Environment Modelsmentioning

confidence: 99%

Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Bounaceur

Khenchaf

Caillec

2022

Sensors

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Along with the rapid development of marine radar, and particularly those carried on aircraft, the detection of small-sized targets which pose an increasing threat has become one of the main areas of interest. However, by considering an observation chain from an aircraft (such as a drone) in a maritime environment, with the aim of detecting and tracking of low signal-to-clutter ratio (SCR) targets, one of the important points would be the analysis of the radar system performance according to the radar input parameters, the atmospheric propagation medium, the various sea clutter characterization, and the type of targets (RCS, speed, etc.) in this environment. Therefore, it is necessary to obtain the overall path loss including the anomalous atmospheric environment, gas attenuation, clouds attenuation, rainfall attenuation, and beam scanning loss. To consider atmospheric attenuations, ITU-R models are used. On another side, because of spikes and dynamic variation properties, sea clutter is generally described by the statistical distribution with long tail and by its wider Doppler spectrum. Conventional algorithms such as those based on statistical models, MTI, and MTD processing are often limited, especially for the target of low speed and low RCS. Therefore, sea clutter, including empirical and statistical models available, is considered to estimate and simulate the impact of radar input parameters, targets RCS, and sea state on detection performance. The Doppler frequency of target echo which can be exploited for coherent processing is described by assuming an adequate scenario of observation geometry.

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Section: Atmospheric Precipitation Losses and Weather Environment Modelsmentioning

confidence: 99%

Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Bounaceur

Khenchaf

Caillec

2022

Sensors

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“…This is because the detection accuracy of long-range radar systems often degrades due to the atmospheric refractivity since the electromagnetic waves emitted from radars propagate along a bent path rather than a straight path. With regard to the atmospheric refractivity, atmospheric conditions are classified as normal, super, sub, and duct [7][8][9][10]. Among these, the duct conditions have been reported to cause a fatal degradation in the detection performance in the case of long-range targets because such conditions increase the gradient of the atmospheric refractivity index above a certain threshold, making it similar to a waveguide [11,12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Jin,

Cho,

Jang

et al. 2024

Remote Sensing

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This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921.

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Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm

et al. 2022

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In this paper, a method for estimating atmospheric refractivity from sea and land clutters is proposed. To estimate the atmospheric refractivity, clutter power spectrums based on an artificial tri-linear model are calculated using an Advanced Refractive Prediction System (AREPS) simulator. Then, the clutter power spectrums are again obtained based on the measured atmospheric refractivity data using the AREPS simulator. In actual operation, this spectrum from measured reflectivity can be replaced with real-time clutter spectrums collected from radars. A cost function for the genetic algorithm (GA) is then defined based on the difference between the two clutter power spectrums to predict the atmospheric refractivity using the artificial tri-linear model. The optimum variables of the tri-linear model are determined at a minimum cost in the GA process. The results demonstrate that atmospheric refractivity can be predicted using the proposed method from the clutter powers.

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Prediction of Target Detection Probability Based on Air-to-Air Long-Range Scenarios in Anomalous Atmospheric Environments

Cited by 4 publications

References 36 publications

Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm

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