The efficiency of a multi-position system depends on the realization of its struc-ture — how many elements it includes, where they are located, and how the envi-ronment and terrain influence its operation. The paper is dedicated to data pro-cessing in a multi-position surveillance system as an additional option, leverag-ing the in-between big data from the system’s elements. A sufficient number of numerical data generated by the multi-position system and its elements–sensors–allows the use of statistical methods and models from machine learning or deep learning. The ontology for quality estimation of the multi-position sys-tem, depending on its configurations, is proposed. The results of the distribu-tions of detected events are presented in graphical forms that allow statistical evaluation of the distributed data. Our findings allow us to ensure the efficiency of a multi-position system in an unpredictable, variable environment by recon-figuring it when it offers better capabilities.
Тимчук et al. (2025) studied this question.