Abstract. This paper introduces an algorithm for the automatic classification of surface targets based on data from passive radar stations. The primary focus is on developing an end-to-end classification approach that minimizes the loss of potential solutions by accounting for all possible classification scenarios of radio-electronic systems (RES) at intermediate stages. To describe the parameters of radio emissions, n-dimensional vectors are utilized, where discrete and continuous parameters are modeled through intervals. This allows for the consideration of emission variability under real-world conditions. The algorithm is grounded on set theory and probabilistic methods, adapted to scenarios where narrow probabilistic distributions are unavailable. The classification process consists of two stages: identifying RES based on their radio emissions and classifying carriers using combinations of RES. The key advantage of the proposed approach is its ability to preserve all classification options and incorporate them into the decision-making process for selecting the recommended solution. Examples of the algorithm’s application illustrate its effectiveness in real-world scenarios. Computational costs are analyzed, and potential directions for further optimization are identified to enable integration into automated surface surveillance systems.

Keywords: classification of surface targets, radio emission classification, passive radar stations, surface situational awareness

For citation: Bocharov P. V., Libman M. S. An algorithm for classifying surface targets using passive radar data. Aerospace Engineering and Technology. 2024. Vol. 2, no. 4, pp. 118–129. DOI 10.52467/2949-401X-2024-2-4-118-129. EDN BCDHLK (In Russian)