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This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – using a particle swarm method in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel. Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems. A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following: – the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment); – the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue); – the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue). It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %

This paper reports an improved method for processing the image of a vehicle's license plate when shooting with a smartphone camera. The method for processing the image of a vehicle's license plate includes the following stages: – enter the source data; – split the video streaming into frames; – preliminary process the image of a vehicle's license plate; – find the area of a vehicle's license plate; – refine character recognition using the signature of a vehicle's license plate; – refine character recognition using the combined results from frames in the streaming video; – obtain the result of processing. Experimental studies were conducted on the processing of images of a vehicle's license plate. During the experimental studies, the license plate of a military vehicle (Ukraine) was considered. The original image was the color image of a vehicle. The results of experimental studies are given. A comparison of the quality of character recognition in a license plate has been carried out. It was established that the improved method that uses the combined results from streaming video frames works out efficiently at the end of the sequence. The improved method that employs the combined results from streaming video frames operates with numerical probability vectors. The assessment of errors of the first and second kind in processing the image of a license plate was carried out. The total accuracy of finding the area of a license plate by known method is 61 % while the improved method's result is 76 %. It has been established that the minimization of errors of the first kind is more important than reducing errors of the second kind. If a license plate is incorrectly identified, these results would certainly be discarded at the character recognition stage.

The method of determining coordinates of an airborne object using ADS-B receivers was improved. The method involves the following sequence of actions: input of initial data, measurement of coordinates of the airborne object by the radar, checking the availability of data about the airborne object obtained from the ADS-B receivers. In the absence of such data, coordinates of the airborne object are determined only from the data of the radar. The airborne object mark was identified according to information from the radar and the ADS-B receivers. Unlike the known methods, the advanced method of determining coordinates of an airborne object by a radar additionally uses information from the ADS-B receivers. The ADS-B receiver signals were experimentally studied. It was found that the ADS-B receiver has received more than 6,000 messages about airborne objects in a single day. It was established that information about the location of the airborne object contained in ADS-B messages was encoded in CPR format. An algorithm for decrypting the ADS-B messages with a global connection of an airborne object to geographical coordinates was presented. An algorithm for detecting signals of onboard transponders of ADS-B airborne objects was presented. Non-standard ADS-B messages from airborne objects were studied. It was suggested that some short non-standard ADS-B messages were received from small and military airborne objects. Accuracy of determining coordinates of airborne objects by the radar with additional use of the ADS-B receiver was estimated. Dependence of the root mean square error of determining the airborne object coordinates on a distance to the airborne object was presented for various cases. It was established that the accuracy of determining the airborne object coordinates can be raised from 36 % to 67 % depending on the distance to the airborne object