基于GA-SA的低轨星座传感器资源调度算法

针对基于低轨预警系统的多目标跟踪,提出了兼顾跟踪精度与系统效率的传感器资源调度算法。首先,建立了目标跟踪模型。然后,以调度周期内后验克拉美罗下界(posterior Cramer-Rao lower bound,PCRLB)变化率、卫星切换率为指标,建立了传感器调度的混合整数规划模型,在此基础上,采用遗传(genetic algorithm,GA) 模拟退火(simulated annealing,SA)混合算法对调度模型进行优化求解,提高了对解空间的搜索能力与求解速度。最后,仿真试验表明本文调度模型的正确性与GA-SA混合优化算法的有效性。

LEO constellation sensor resources scheduling algorithm based on Genetic and Simulated annealing

A sensor resources scheduling algorithm is proposed to improve the track accuracy and system work effectiveness of multi-target tracking problem based on warning system of LEO. Firstly, the target tracking model is constructed. Then, with two contradictory objectives, the PCRLB change and switch rate of satellite during the scheduling period, a mixed integral programming model is founded. Based on the model, a mixed genetic algorithm and simulated annealing is proposed to solve the sensor scheduling problem. The algorithm improves the seeking ability and convergence speed. Finally, simulations demonstrate the correctness of the model and the validity of GA-SA algorithm.