• Title/Summary/Keyword: 누적탐지확률

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Calculation of the Detection Range for a Given Cumulative Probability in Airborne Surveillance Radars (탐색 레이다에서 누적확률에 기인한 탐지거리 계산에 관한 연구)

  • Kim, Eun Hee;Roh, Ji-Eun
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.29 no.1
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    • pp.24-27
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    • 2018
  • The performance measure of airborne radars is the range at which the cumulative probability of detection has some specified value, because the per-scan detection probability is an oscillatory function of the target range in airborne radars operating with the dynamic clutter environment. As a result, no one range, at which the per-scan detection probability has a given value, can give a meaningful description of the range performance. In this paper, we provide the equation to calculate the cumulative detection probability and show that the result of Monte Carlo simulation is same as the calculated value in a simple scenario. This verified Monte Carlo model will be used to evaluate the performance of airborne radars in various operating scenarios, at which the numerical calculation is difficult.

A Study on Design and Analysis of an Alert-Confirm Detection Method (Alert-Confirm 탐지 방식의 설계 및 성능 분석에 관한 연구)

  • Eunhee Kim;Hyunsu Oh;Sawon Min
    • Journal of the Korea Institute of Military Science and Technology
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    • v.27 no.2
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    • pp.140-146
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    • 2024
  • Active electronically scanning antennas are faster and more flexible in beam-scheduling than mechanical antennas. Thus, they require an advanced resource management or detection methods to operate efficiently. In a surveillance radar performing periodic detection, alert-confirm detection is an excellent method to improve the cumulative detection probability by reducing the period while maintaining the detection probability. This paper proposes a design method for alert-confirm detection based on the parameters of the conventional design. We developed a simulator based on simulink@matworks and verified the result through Monte Carlo simulation.

Measure of Effectiveness for Detection and Cumulative Detection Probability (탐지효과도 및 누적탐지확률)

  • Cho, Jung-Hong;Kim, Jea Soo;Lim, Jun-Seok;Park, Ji-Sung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.15 no.5
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    • pp.601-614
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    • 2012
  • Since the optimized use of sonar systems available for detection is a very practical problem for a given ocean environment, the measure of mission achievability is needed for operating the sonar system efficiently. In this paper, a theory on Measure Of Effectiveness(MOE) for specific mission such as detection is described as the measure of mission achievability, and a recursive Cumulative Detection Probability(CDP) algorithm is found to be most efficient from comparing three CDP algorithms for discrete glimpses search to reduce computation time and memory for complicated scenarios. The three CDPs which are MOE for sonar-maneuver pattern are calculated as time evolves for comparison, based on three different formula depending on the assumptions as follows; dependent or independent glimpses, unimodal or non-unimodal distribution of Probability of Detection(PD) as a function of observation time interval for detection. The proposed CDP algorithm which is made from unimodal formula is verified and applied to OASPP(Optimal Acoustic Search Path Planning) with complicated scenarios.

Optimal Acoustic Search Path Planning Based on Genetic Algorithm in Discrete Path System (이산 경로 시스템에서 유전알고리듬을 이용한 최적음향탐색경로 전략)

  • CHO JUNG-HONG;KIM JUNG-HAE;KIM JEA-SOO;LIM JUN-SEOK;KIM SEONG-IL;KIM YOUNG-SUN
    • Journal of Ocean Engineering and Technology
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    • v.20 no.1 s.68
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    • pp.69-76
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    • 2006
  • The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

Analysis of Optimal Infiltraction Route using Genetic Algorithm (유전자 알고리즘을 이용한 최적침투경로 분석)

  • Bang, Soo-Nam;Sohn, Hyong-Gyoo;Kim, Sang-Pil;Kim, Chang-Jae;Heo, Joon
    • Korean Journal of Remote Sensing
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    • v.27 no.1
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    • pp.59-68
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    • 2011
  • The analysis of optimal infiltration path is one of the representative fields in which the GIS technology can be useful for the military purpose. Usually the analysis of the optimal path is done with network data. However, for military purpose, it often needs to be done with raster data. Because raster data needs far more computation than network data, it is difficult to apply the methods usually used in network data, such as Dijkstra algorithm. The genetic algorithm, which has shown great outcomes in optimization problems, was applied. It was used to minimize the detection probability of infiltration route. 2D binary array genes and its crossover and mutation were suggested to solve this problem with raster data. 30 tests were performed for each population size, 500, 1000, 2000, and 3000. With each generation, more adoptable routes survived and made their children routes. Results indicate that as the generations increased, average detection probability decreased and the routes converged to the optimal path. Also, as the population size increases, more optimal routes were found. The suggested genetic algorithm successfully finds the optimal infiltration route, and it shows better performance with larger population.

A Case Study: Improvement of Wind Risk Prediction by Reclassifying the Detection Results (풍해 예측 결과 재분류를 통한 위험 감지확률의 개선 연구)

  • Kim, Soo-ock;Hwang, Kyu-Hong
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.3
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    • pp.149-155
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    • 2021
  • Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

Improvement and Validation of Convective Rainfall Rate Retrieved from Visible and Infrared Image Bands of the COMS Satellite (COMS 위성의 가시 및 적외 영상 채널로부터 복원된 대류운의 강우강도 향상과 검증)

  • Moon, Yun Seob;Lee, Kangyeol
    • Journal of the Korean earth science society
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    • v.37 no.7
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    • pp.420-433
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    • 2016
  • The purpose of this study is to improve the calibration matrixes of 2-D and 3-D convective rainfall rates (CRR) using the brightness temperature of the infrared $10.8{\mu}m$ channel (IR), the difference of brightness temperatures between infrared $10.8{\mu}m$ and vapor $6.7{\mu}m$ channels (IR-WV), and the normalized reflectance of the visible channel (VIS) from the COMS satellite and rainfall rate from the weather radar for the period of 75 rainy days from April 22, 2011 to October 22, 2011 in Korea. Especially, the rainfall rate data of the weather radar are used to validate the new 2-D and 3-DCRR calibration matrixes suitable for the Korean peninsula for the period of 24 rainy days in 2011. The 2D and 3D calibration matrixes provide the basic and maximum CRR values ($mm\;h^{-1}$) by multiplying the rain probability matrix, which is calculated by using the number of rainy and no-rainy pixels with associated 2-D (IR, IR-WV) and 3-D (IR, IR-WV, VIS) matrixes, by the mean and maximum rainfall rate matrixes, respectively, which is calculated by dividing the accumulated rainfall rate by the number of rainy pixels and by the product of the maximum rain rate for the calibration period by the number of rain occurrences. Finally, new 2-D and 3-D CRR calibration matrixes are obtained experimentally from the regression analysis of both basic and maximum rainfall rate matrixes. As a result, an area of rainfall rate more than 10 mm/h is magnified in the new ones as well as CRR is shown in lower class ranges in matrixes between IR brightness temperature and IR-WV brightness temperature difference than the existing ones. Accuracy and categorical statistics are computed for the data of CRR events occurred during the given period. The mean error (ME), mean absolute error (MAE), and root mean squire error (RMSE) in new 2-D and 3-D CRR calibrations led to smaller than in the existing ones, where false alarm ratio had decreased, probability of detection had increased a bit, and critical success index scores had improved. To take into account the strong rainfall rate in the weather events such as thunderstorms and typhoon, a moisture correction factor is corrected. This factor is defined as the product of the total precipitable waterby the relative humidity (PW RH), a mean value between surface and 500 hPa level, obtained from a numerical model or the COMS retrieval data. In this study, when the IR cloud top brightness temperature is lower than 210 K and the relative humidity is greater than 40%, the moisture correction factor is empirically scaled from 1.0 to 2.0 basing on PW RH values. Consequently, in applying to this factor in new 2D and 2D CRR calibrations, the ME, MAE, and RMSE are smaller than the new ones.