The Development of the Method of Radar Observation System Construction of the Airspace on the Basis of Genetic Algorithm

Олексенко, О. О.; Khudov, Hennadii; Petrenko, Kyrylo; Horobets, Yurii; Kolianda, Valerii; Kuchuk, Nina; Konstantinov, A. M.; Kireienko, Volodymyr V.; Сердюк, О. В.; Yuzova, Iryna; Solomonenko, Yuriy

doi:10.46338/ijetae0821_04

Cited by 14 publications

(24 citation statements)

References 16 publications

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“…In Fig. 1, some notations are borrowed from [25,26] The method of segmentation of camouflaged military equipment in images from space surveillance systems using a genetic algorithm involves the following main stages:…”

Section: The Main Stages Of the Methods Of Segmentation Of Camouflage...mentioning

confidence: 99%

“…The function ( ) t l g φ was introduced for the following reasons. It is known [25,26] that the genetic algorithm uses operators of selection, crossover, mutations of chromosomes. At the same time, it is possible when these operators can be applied to pixels of segments that do not correspond to a certain number of segments K. The probability of such an event is quite high due to the random nature of the choice of component values in the formation of the initial population of chromosomes.…”

Section: Calculate Function ( )mentioning

confidence: 99%

“…1. The main stages of the method of segmentation of camouflaged military equipment in images from space surveillance systems using a genetic algorithm for further recovery according to the principle of "survival of the best" using the "virtual roulette" procedure [25,26]. This procedure allows chromosomes with a greater value of the objective function to be selected more likely.…”

Section: Calculate Function ( )mentioning

confidence: 99%

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Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

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The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems. The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – the use of a genetic algorithm in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space. Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems. A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way: – compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind; – compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind. The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

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Section: The Main Stages Of the Methods Of Segmentation Of Camouflage...mentioning

confidence: 99%

Section: Calculate Function ( )mentioning

confidence: 99%

Section: Calculate Function ( )mentioning

confidence: 99%

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Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

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“…In [32], the RS construction based on a genetic algorithm is proposed. It has been established that the genetic algorithm makes it possible to build a highquality RS.…”

Section: Mathematicsmentioning

confidence: 99%

“…It has been established that the genetic algorithm makes it possible to build a highquality RS. But in [32], the RSs operate autonomously; the RS operation as part of a multiposition system is not considered.…”

Section: Mathematicsmentioning

confidence: 99%

Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare

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The increase in the accuracy of determining the coordinates of targets is explained by the use of a network of counter-battery radar stations and the rangefinding method for determining the coordinates of targets. The main advantage of using the rangefinding method for determining the coordinates of targets in a network of counter-battery radar stations is to ensure the required accuracy in determining the coordinates of targets without using accurate measurement of angular coordinates. The minimum geometry of the system, which ensures the use of the rangefinding method for determining coordinates, is given. The method of determining the coordinates of targets by a network of counter-battery radar stations has been improved. In contrast to the known ones, information about the range to the target is additionally used in a spatially distributed network of radar stations for counter-battery combat. The boundaries of the working zones of the network of two and three counter-battery radar stations are calculated. The features of creating a continuous strip using the rangefinding method for determining the coordinates of the target are considered. Statistical modeling of the rangefinding method for determining the plane coordinates of the target has been carried out. It has been established that the use of the rangefinding method ensures the determination of the planar coordinates of the target in a sector of at least 120°. The targets are at a distance of direct radio visibility in relation to the counter-battery radar. The root-mean-square error in determining the target range in this case is no more than 50 m. It has been established that the creation of continuous bands of a low-altitude radar field at a certain height is possible by arranging radar stations in a line. In this case, the distance between the counter-battery radar stations should be no more than half the target detection range at this height

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Methods of UAVs images segmentation based on k-means and a genetic algorithm

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Khudov

Makoveichuk

et al. 2022

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The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles. The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that: – the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively; – average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively. It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.

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The Development of the Method of Radar Observation System Construction of the Airspace on the Basis of Genetic Algorithm

Cited by 14 publications

References 16 publications

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare

Methods of UAVs images segmentation based on k-means and a genetic algorithm

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