Synthesis of an Algorithm for Processing the Trajectories of Moving Objects Using the Methods of Data Clustering Theory

Introduction. Requirements for the quality of information about the trajectory of moving objects provided by sensor networks are increasingly becoming more stringent. For Information and Data Processing Centers (DPC) at control and management command posts, the issue of information mapping and forming the true trajectories of moving objects in the area of intersection of network detection zones is of particular importance. The use of conventional approaches to solving this problem involves issues related to ensuring the efficient provision of users with complete and reliable information about trajectories in real time. In this article, wee propose a new approach to solving this problem using data mining theory, in particular, the methods of data clustering theory. Based on an analysis of the process of processing radar data in a DPC and its similarity with that of data clustering, we synthesized an algorithm for processing the trajectories of moving objects. The algorithm was verified by modelling and experimental research.Aim. To develop a generalized scheme for processing object trajectories (TP) in a DPC and to synthesized a TP algorithm using the methods of data clustering theory.Materials and methods. Data Clustering theory, Systems Engineering theory, Radar Data processing theory (RD), methods of mathematical modelling and experimental research.Results. Based on an analysis of the essence of radar data processing (RD) in a DPC and its similarity with the process of data clustering, an algorithm for processing the trajectories of moving objects was synthesized and verified by modelling and experimental research. A generalized scheme for processing the trajectories of moving objects in a DPC and a TP algorithm for a DPC were synthesized.Conclusions. An algorithm for processing object trajectories was proposed based on a new approach of data clustering theory. A generalized scheme and an algorithm for processing object trajectories (TP) in a DPC were suggested. These developments can be effectively applied in various models, e.g. centralized, hierarchical and decentralized. The synthesized algorithm can provide output information about the true identified trajectories in terms of various indicators of data processing systems (DPS).

Neural network processing of radar scanning data array

This article deals with the digital processing of a matrix radar image. The information received from the radar scanner needs to be transformed to enable visual perception. The article describes the main methods of digital processing of matrix data, presents the images transformed by them. The aim of the article was the development of a radar data processing algorithm that identifies the contours and edges of examined objects. The authors propose an algorithm for isolating the geometric structure of the scanned area. The difference between the processing method and the known analogues is based on the nature of the change in the values of the array being processed and consists in the double operation of extracting the gradient of the distribution of values. The software implementation of the algorithm is made in C++ using methods from an open library of computer vision. The efficiency of the algorithm was estimated based on comparison with the algorithms for determining edges based on linear filtering and neural networks. The results of the work can be used to create software for mobile short-range radar devices. Imaging from object boundaries and their edges provides spatial perception of the image by the operator, and free areas are available for rendering additional information. This solution allows you to combine scanning devices and thereby increase the information value of the result.

Simulation model of radar data processing in a station network under signal-like interference

Introduction: Radar stations, when tracking targets in a complex interference environment, form not only target marks but also false marks. A well-developed theory and technique of noise stability is not useful under signal-like interference caused by re-reflections, multi-path propagation or retransmission of the probing signals. The reliability of radar information processing under signal-like interference can be improved by joint processing of data from several spaced posts in a radar station network. Purpose: development of а simulation model which would allow you to estimate the effectiveness of radar target selection by spatial rating of its measured positions, with joint processing of the radar information obtained from two spaced radar stations. Results: We have implemented the framework of joint radar data processing for target selection in a radar station network under signal-like interference. The selection is based on using the information about the coincidence of radar target coordinates measured by spaced radar stations. A simulation model is developed to estimate the target selection probability under signal-like interference during the joint processing of data from two spaced radar stations, by analyzing the coincidence of the measured coordinates of the targets. It has been found out how the target selection probability depends on the noise interference power and the average density of false marks in the range channels of two spaced radar stations. Practical relevance: The simulation results demonstrate the possibility of increasing the range of radar target detection by network radar stations under signal-like interference, and the efficiency of using the information about coincidence of radar target coordinates measured by spaced radar stations, which is better than using only the signal features of radar target selection on the background of false marks.