Mission Scheduling of Multi-Sensor Collaborative Observation for Space Surveillance Network

With increased dependence on space assets, scheduling and tasking of the surveillance network (SSN) are vitally important. The multi-sensor collaborative observation (MCOS) problem is a multi-constraint high-conflict complex combinatorial optimization that non-deterministic polynomial (NP)-hard. This research establishes sub-time window constraint satisfaction (STWCSP) model with objective maximizing profit. Considering significant effect genetic algorithms (GA) solving resource allocation, an evolution heuristic (EH) algorithm containing three strategies focus MCOS proposed. For each case, task sequence first obtained via improved GA penalty (GAPE) algorithm, then mission planning (heuristic rule) used to determine specific time. Compared without windows some other algorithms, series experiments illustrate STWCSP has better performance in terms total Experiments about strategy parameter sensitivity validate its excellent EH algorithms.