Multi-sensor cooperative planning of space objects detection

Background of collaborative detection of space targets by multiple ground-based radars, to solve the issue of low detection efficiency in traditional collaborative planning methods that use the entire detectable arc segment as the decision variable, a multi-sensor collaborative detection scheduling model was established, and an adaptive immune genetic algorithm that could simultaneously determine the detection arc segment and detection start time was proposed. Considering various factors such as the space objects attribute, type, launch time, radar cross-section grade, and purpose, a multi-level fuzzy comprehensive evaluation model was constructed, and the 1-9 scale method was adopted to obtain the priority of the spatial target. In order to maximize the priority, consideringvarious constraints such as detection time, sensor capacity, and so on, an adaptive immune genetic algorithm was used to solve the problem. The performance of the planning method was evaluated from two aspects of detection resource consumption rate and task completion rate. By comparative analysis with the improved heuristic algorithm and traditional evolution algorithm, this algorithm improves the task completion rate while also reducing resource consumption rate.