Task Scheduling Optimization for Comprehensive Adaption to Performance Requirement

The resource requirement for sensor task processing increases due to factors such as the increase in task types/numbers and the enhancement of performance requirements. Sensors enters saturated resource requirement state as the requirements exceed the total available resources. To address the simultaneous multi-task processing in scenario of saturated resource requirement, a sensor task scheduling optimization is formulated with purpose of comprehensive adaption to excepted performance requirements for all pending tasks. An iterative algorithm is also proposed with alternating joint adjustment of different resources parameters. Simulation experiments are designed using passive sensor tracking of multiple emitters to verify the proposed algorithm. Simulation results demonstrate that compared with existing methods, the task scheduling designed by the proposed algorithm can more comprehensively meet the expected performance requirements of all pending tasks, avoid excessive allocation of resources to high-priority tasks, and can also stably adapt to various task scenarios, supporting the sensor to achieve stable performance maintenance as required of multiple tasks under resource-saturated conditions.