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This paper addresses scheduling algorithms to determine optimum utilization of an Airborne Early Warning (AEW) radar timeline resource based on radar constraints. The operation of an AEW surveillance radar in dense overland environments along with the presence of low altitude as well as highly maneuvering targets make detection and tracking a very complex one. A mechanically rotating antenna with electronic scanning capability addresses this problem. Not only does it provide maximum gain in the boresight direction of the antenna, but also the flexibility to focus energy and provide higher update rates at given sectors and selected targets. With the advent of electronic scanning, an efficient means of utilizing the radar timeline and waveforms with the available radar resources is required. To do this, a radar resource management concept is required for future AEW electronic scanning surveillance systems. This paper studies the resource management problem for an antenna with electronic scanning capabilities without rotation. The timeline is formulated in terms of radar dwells and revisit time constraints specified for each surveillance sector. A dwell is defined as radar time on target or angular position and revisit time is defined as the time between radar updates of a particular target or angular position. The methodology provides a criterion for determining if a feasible schedule exists that satisfies the dwell and revisit time constraints as well as methods for computing such schedules. The investigation includes the structure of optimal schedules and the complexity of the problem. Several solution techniques have been developed. The first algorithm developed is exact and it is based on dynamic programming. Since the problem is NP-hard, this algorithm is efficient for a small number of sectors. In order to solve medium and large size problems, heuristic approaches have been pursued. The heuristic developed is based on constrained semi-Markov decision processes. First, a relaxed version of the problem utilizing average re-visit time constraints is used rather than solving the problem in a rigorous way. Search methods are then used to find a rigorous solution.
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