Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary.

The paper presents a scientific and technical substantiation of the parameters of the radar device, which allows to detect and visualize forbidden objects by means of electromagnetic radiation of the microwave range. A model of a multistatic scheme for measuring waves reflected from an object based on the holographic method is developed. A software complex of simulation modeling of radar device operation has been developed, by means of which reconstructions of object images for different configurations of sparse sensor subsystems of the radar device have been obtained. An optimal configuration of the sparse sensor subsystem was selected by the method of peak signal-to-noise ratio analysis, for which a multi static hologram was modeled and a reconstruction of the metal rod image was generated. The reconstruction of the metal rod image obtained as a result of simulation modeling has a recognizable contour, which confirms the prospect of developing an experimental prototype of a radar device for detecting prohibited objects.