实际复杂约束条件下多星区域覆盖优化算法

MULTI-SATELLITE REGION COVERAGE OPTIMIZATION ALGORITHM UNDER REAL COMPLEX CONSTRAINTS

利用多星协同对大面积区域成像侦测,要求在最短的时间内完成对给定目标区域的完全覆盖,得到卫星每次过境最优侧视观测方案。现有覆盖算法具低效耗时技术瓶颈,有空间覆盖率及时效性局限。充分利用卫星每次过境观测机会,综合考虑相机分辨率、太阳高度角、地面精度等约束条件,依据不同卫星侧摆能力、传感器可视性能以及卫星轨道参数特征,对区域目标进行量化覆盖分析,建立网格划分模型,提出基于一次覆盖策略的覆盖优化算法。该算法能适应实际复杂约束条件,在实际问题规模下,具有良好的效率,能满足工业精度要求。在仿真应用中检验了该算法的有效性和适应性。

Using multi-satellite co-operation to detect large area, it is required to complete the full coverage of a given target area in the shortest time, and obtain the optimal side-view observation scheme for each transit. The Existing coverage algorithm has the disadvantages of time-consuming and low efficiency technology bottleneck, spatial coverage rate and time limitations. In this paper, we took full advantage of the opportunity of each transit observation of the satellite, considering the constraints such as camera resolution, solar elevation angle and ground precision. Based on the characteristics of satellite side swing, sensor visual performance and satellite orbit parameters, the regional target was covered by quantitative coverage analysis, and the grid partitioning model was established. A coverage optimization algorithm based on one coverage strategy was proposed, which could adapt to the practical complex constraints conditions, in the actual problem scale, with good efficiency, to meet the industrial accuracy requirements. Finally, the effectiveness and adaptability of the algorithm were verified in the simulation application.