A Genetic-Algorithm Approach to Scheduling Communications for Embedded Parallel Space-Time Adaptive Processing Algorithms

Computational efficiency is of great significance for high-performance embedded applications. The work here develops and evaluates a geneticalgorithm-based (GA-based) optimization technique for the scheduling of messages for a class of parallel embedded signal processing techniques known as space–time adaptive processing (STAP). The GA-based optimization is performed off-line, resulting in static schedules for the compute nodes of the parallel system. These static schedules are utilized for the on-line implementation of the parallel STAP application. The primary motivation and justification for devoting significant off-line effort to solving the formulated scheduling problem is the resulting reduction of hardware resources required for the actual on-line implementation. Numerical studies illustrate that reductions in hardware requirements of around 50% can be achieved by employing the results of the proposed scheduling techniques. This reduction in hardware requirement is of critical importance for STAP, which is typically an airborne application in which the size, weight, and power consumption of the computational platform are severely constrained. # 2002 Elsevier Science (USA)