Dual face phased array radar scheduling with multiple constraints

Abstract

Tasks in many real-time applications can be scheduled by variations of rate monotonic or earliest deadline first algorithms. When this is possible, it is satisfying to have formal analysis and performance bounds underlying the use of these algorithms. However, in many applications the simultaneous set of constraints that must be satisfied makes these traditional solutions unsuitable. Practical solutions for these more complicated applications are important. In this paper we develop a novel integrated scheduling and allocation heuristic for a dual face phased array radar system. The realistic features of the radar system that must be simultaneously addressed include timeliness (worst case execution time, period, deadline), semantic importance, and physical constraints such as beam selection and frequency harmonics. The heuristic function we develop provides a very flexible way to incorporate these requirements into one single equation. Since scheduling high semantic importance tasks is paramount, we use the highest semantic importance tasks' success ratio as the major performance metric. Based on simulation results, we show that our static heuristic algorithm can schedule more than 91% of the highest semantic importance tasks at high frequency conflict degree even at heavy workloads. The result is 50% better than EDF and 31% better than an importance (IMP) based static priority scheduling algorithm where IMP is similar to various current approaches. For the online scheduling algorithm, our heuristic algorithm is 30% better than EDF and 20% better than IMP in terms of highest semantic importance tasks' success ratio at heavy workloads.