In this paper we consider the task of optimal resource management in multi-position radar systems. On the basis of hierarchical control system we consider the control level at which reallocation of an active radars is performed, depending on the operational situation in area of responsibility of multi-position radar system. We propose a fast algorithm, which can be used for finding a suboptimal solution of the optimization task on this level.

In this paper we consider the resource management task in closely spaced multiposition radar systems. The aim of the work is to develop an investigate approaches of avoiding resource overuse and mutual interference of the radars. We consider two levels of resource management: reallocation of active radars depending on the operational situation in area of responsibility the system to minimize used radar resources, and reallocation of frequency resources between active radars to avoid mutual interference. Fast suboptimal algorithms are proposed for both levels of optimization.

In this paper we propose and study the frequency management algorithm for closely-spaced multiposition radar systems. The aim of the work is to develop an investigate algorithm for preventing mutual interference of closely-spaced radars, that can be done by proper use of limited frequency resources. We propose to achieve minimization of interference by creating of frequency schedule for each radar. To do this, we propose to predict conflicts, and find time intervals, where all conflicting radars can have different frequencies and therefore conflicts can be resolved by frequency reallocation. The numerical simulations were done to test performance and computational load of the proposed algorithm.

This paper considers a numerically stable discrete-time representation of the three-dimensional Constant Turn (CT) motion model within the Interacting Multiple Model (IMM) framework for radar tracking of maneuvering aerial targets. Classical discrete CT models used in Kalman-filter-based tracking contain singular expressions in the vicinity of zero and near-zero turn rates, which may degrade estimation accuracy and impair numerical robustness. To address this problem, a Maclaurin-series-based discretization of the three-dimensional CT model is developed, in which the state transition matrix and the process-noise-related matrices are approximated in polynomial form. Linear, quadratic, and cubic approximations are constructed and analyzed. The proposed CT model is integrated into a three-model IMM algorithm together with the Constant Velocity (CV) and Constant Acceleration (CA) models. The study includes both an internal comparison of Maclaurin approximations of different orders and an external comparison with the classical CT discretization and a Padé-based reference discretization. Numerical experiments are performed for representative three-dimensional maneuvering scenarios under radar measurement conditions. The obtained results show that the proposed discretization eliminates singular behavior near zero turn rate while preserving the tracking capability of the IMM estimator. The comparative analysis demonstrates that the quadratic Maclaurin approximation provides the most favorable trade-off between modeling accuracy, numerical stability, and computational cost. It yields tracking performance close to higher-order approximations and competitive with the Padé-based reference approach, while remaining simpler for practical implementation in real-time radar tracking systems. These results indicate that the proposed quadratic approximation is a suitable solution for maneuvering aerial target tracking in three-dimensional radar applications.