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Radar resource allocation optimization in phased array radar systems

Serbest, İzzet
The demand for enhanced radar technologies has grown while mission requests have become more complex. Development of Active Electronically Scanned Array (AESA) Technologies has created enormous functional achievements. Development of radar platforms has led to the radar resource allocation issues and adaptive Radar Resource Management (RRM) studies for Multi-Function Radars. Combining the functionality of different tasks in one special device also makes resource allocation process more challenging due to its comprehensive capabilities. Such complicated systems have become an example of technology in which multiple tasks can share multiple resources in order to satisfy their requirements. Therefore, resource optimization strategy is becoming more crucial for radar systems. This thesis is mainly focused on radar resource allocation in order to ensure optimization of radar resources in an efficient way. A proposed resource allocation approach described in [1] is applied in detail. Optimization-based measurement policies are studied for online beam scheduling in real-time. Radar tasks by which resource allocation is held are approached like series of independent tracking and searching subtasks in the system. Using the independent subtask approach makes optimization easier and converts it to a known general integer linear programming problem. The optimization problem is modeled to maximize overall utility function based on tracking quality in real time while meeting resource constraints. Connection of radar tasks is handled via constraints of the resources, and the constraints are included in a resource allocation algorithm using Lagrange relaxation method. In addition, different performance measures are used in optimization to reflect different aspects which are important at the slow time level. As an example, implementation and testing of tracking in clutter using Probabilistic Data Association is studied. Using PDA filter, control of the gating thresholds gives rise to a different optimization problem solution.