Resource Allocation in Airborne Surveillance Radar

Radars equipped with an Electronically Scanned Antenna (ESA) have the potential of directing the electromagnetic radar beam without mechanically adjusting the antenna. Furthermore, the beam can be redirected instantaneously towards any location in space. Hence, the mechanical constraint of a traditional antenna, which imposes the typically revolving measurement pattern, is relaxed. This means, for example, that targets can be observed in any order. The <i>radar resource management</i> problem is now to design a measurement policy, which optimally utilizes the ESA potentials. This problem is a highly complicated, stochastic optimization problem, and in existing work, many of the practical aspects from real-life are disregarded. For radar designers, practical aspects are important, and more work is needed that focuses on the practical aspects. <p />In this thesis, optimization-based measurement policies are studied both for offline analysis, and for online beam scheduling in real-time. The general approach in the thesis is to solve a simplified problem denoted as <i>resource allocation</i>, which is posed on a higher abstraction level and on a slower time scale than the original resource management problem. It turns out that the resource allocation problem naturally separates into a series of independent tracking and searching subtasks in the radar system. Among these subtasks, the antenna is regarded as a limited and time shared resource. Given shares of radar resources, i.e., time or energy, each subtask can be optimized locally. Coordination of the subtasks is handled via constraints on the resources, where the constraints are included in a resource allocation algorithm using Lagrange relaxation. The resulting algorithms are tested in simulations, given models and performance measures appropriate to the abstraction level in question. <p />In an initial work on high-level resource allocation, an approach designed to detect whether a target navigates along a known route, is studied. The knowledge regarding a target's choice of trajectory is applicable in high-level resource allocation when deciding on target priorities