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

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