<title>Sensor resource management for an airborne early warning radar</title>

Abstract

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 NPhard, 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.

Scheduling Beams with Different Priorities on a Military Surveillance Radar

“…Feinberg, et al [6] consider a scheduling problem arising in the context of a nonrotating radar with electronic scanning capabilities. They propose an exact dynamic programming mechanism and several heuristics to investigate whether or not a feasible schedule that satisfies the dwell and revisit time constraints can be found.…”

Windows scheduling of arbitrary length jobs on parallel machines

“…Example: Let I = (4, 2), (8,4), (8,2), (16,4), (16,4) for jobs a, b, c, d, e, respectively. By calculating the total processing requirements of the jobs, it is clear that more than one machine is needed.…”

Windows scheduling of arbitrary-length jobs on multiple machines

“…Since a restricted version of the optimal windows scheduling problem is NP-hard even for one machine , 2007Feinberg et al 2002), we look for approximate solutions. A natural lower bound to the windows scheduling problem is the total width of the jobs.…”

“…Closely related problems from the operation research and the communication networks areas include the machine maintenance problem (Wei and Liu 1983;Anily et al 1998), the multi-item replenishment and other inventory problems (Hadley and Whitin 1963;Roundy 1985;Hassin and Megiddo 1991), and the sensor resource management problem (Feinberg et al 2002).…”

“…Previous results for the windows scheduling problem considered either the case of one machine with unit-length jobs (the pinwheel problem, Holte et al 1989Holte et al , 1992, or the case of unit-length jobs with multiple machines (BarNoy and Ladner 2003;Bar-Noy et al 2007), or the case of one machine with arbitrary-length jobs (the generalized pinwheel problem, Baruah and Lin 1998;Feinberg et al 2002;Feinberg and Curry 2005). This paper considers the generalized windows scheduling problem of multiple machines with arbitrary-length jobs.…”