遥感卫星星群任务规划算法通用测评框架

李今飞; 余晓刚; 田菁; 何浩辰; 邢相薇; 张筱晗

doi:10.11999/JEIT260335

遥感卫星星群任务规划算法通用测评框架

doi: 10.11999/JEIT260335 cstr: 32379.14.JEIT260335

北京市遥感信息研究所北京 100011

详细信息

作者简介:
李今飞：男，硕士，副研究员，研究方向为卫星信息智能处理与应用技术

余晓刚：男，博士，研究员，研究方向为卫星信息智能处理与应用技术

田菁：女，博士，研究员，研究方向为卫星信息智能处理与应用技术

何浩辰：男，博士，助理研究员，研究方向为卫星信息智能处理与应用技术

邢相薇：男，博士，副研究员，研究方向为卫星信息智能处理与应用技术

张筱晗：女，博士，助理研究员，研究方向为卫星信息智能处理与应用技术

通讯作者:
李今飞　365717802@qq.com

中图分类号: TP391; TP18
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出版历程
- 修回日期: 2026-05-29
- 录用日期: 2026-05-29
- 网络出版日期: 2026-06-10

A general evaluation framework for mission planning algorithms for remote sensing satellite constellations

Institute of remote sensing information , Beijing100011, China

摘要

摘要: 当前卫星遥感已成为国土资源普查和防灾减灾的关键工具，国家经济发展对遥感的需求也快速增长，卫星数量规模不断扩展，遥感星群资源的任务规划显得尤为重要，星群任务规划算法面临从单星静态调度到星群(包含多个异构混合星座)动态协同的范式跃迁。星群任务规划需要根据各颗卫星轨道分布、载荷性能和约束条件，综合考虑任务位置分布、时效频次等要求，为每颗卫星制定观测计划，实现星群整体观测效益最大化。然而当前学术界对遥感卫星星群任务规划算法测评尚未形成统一的量化标准，不同研究涉及的卫星规模从数十颗到数百颗不等，任务数量从数百到数千跨度巨大，这使得跨论文的性能比较几乎不可能。本文提出面向遥感卫星星群任务规划算法的通用测评框架 “Remote Sensing Constellation Mission Planning Benchmark (RSCMP-Bench)”，主要贡献体现为三个方面：多场景标准任务库、多维效能评估指标体系、仿真与评测平台。通过开展基准测试，验证了RSCMP-Bench测试流程的可行性、可复现性及其对算法性能评估的区分能力。测评框架及运行环境已发布在天智杯在线平台，RSCMP-Bench旨在为领域建立类似ImageNet之于计算机视觉、GLUE之于自然语言处理的统一基准，推动卫星任务规划算法从“孤立实验”走向“标准化竞技”，最终加速面向下一代智能遥感星群的规划技术突破。
- 遥感卫星星群 /
- 任务规划算法 /
- 测评框架
Abstract: Objective The rapid proliferation of remote sensing satellites has shifted mission planning from single-satellite static scheduling to large-scale dynamic coordination of heterogeneous constellations. Despite algorithmic advances, the field lacks standardized benchmarks—existing studies use private datasets, oversimplified metrics (mainly task completion rate), and idealized simulations that ignore real constraints like attitude maneuvers, illumination, and dynamic task insertions. This fragmentation prevents cross-paper comparison and hinders operational translation. To bridge this gap, we propose RSCMP-Bench, a general, open, reproducible evaluation framework serving as a unified community standard, analogous to ImageNet for vision and GLUE for NLP. Methods RSCMP-Bench has three pillars: 1)Multi-scenario task library: 300 standardized scenarios across three difficulty levels (Low/Medium/High, 100 each). Satellite counts range from 30 (Low) to 200 (High); task numbers from 50 to over 500. All scenarios use public TLE data and explicitly model constraints: optical satellites require minimum solar elevation, SAR satellites have incidence angle ranges and thermal control intervals. Task types include point and area targets. 2)Multi-dimensional evaluation: Basic Performance layer (completion rate, weighted completion, average response delay, time utilization) and a Dynamic Adaptability layer using a multi-stage rolling horizon with random task insertions. The Dynamic Adaptability Score averages post-insertion completion rate relative to baseline; Dynamic Response Efficiency measures gain per replanning time. A composite RSCMP-Bench Score is also provided. 3)Simulation Evaluation Platform: Client-server architecture with SGP4 propagator, algorithm adapters, two-stage constraint verification, intelligent scenario generator, and visualization. Deployed at https://www.tianzhibei.com, it has supported a national competition with over 80 teams. Results and Discussions Baseline experiments (Random Scheduler vs. Priority Greedy) validate feasibility, discriminative power, and reproducibility. Random Scheduler achieved very low completion rates (7.3% on Low, 3.8% on Medium, 1.9% on High), confirming extreme sparsity of the feasible solution space. Priority Greedy performed substantially better but showed clear degradation: completion rates dropped from 76.1% (Low) to 63.7% (Medium) to 49.2% (High), demonstrating that high-difficulty scenarios challenge even reasonable heuristics and leave ample room for advanced algorithms. The dynamic adaptability protocol successfully quantified robustness under unexpected task insertions—a metric absent from static evaluations. The two-stage constraint verification rejected all invalid plans, generating detailed error reports for debugging. Conclusions RSCMP-Bench establishes the first unified, fair, reproducible benchmark for remote sensing constellation mission planning. By providing a public library of 300 standardized scenarios, a multi-metric evaluation system (basic performance + dynamic adaptability), and a simulation platform with realistic constraints and automated scenario generation, it directly addresses the field’s long-standing “evaluation vacuum.” Baseline results confirm its discriminative power and reveal substantial challenges in large-scale dynamic scenarios. Inspired by ImageNet and GLUE, RSCMP-Bench aims to catalyze systematic, community-driven competition. The framework is already deployed at https://www.tianzhibei.com, and the authors invite the community to adopt and contribute, accelerating breakthroughs in intelligent planning for massive remote sensing constellations.
- Remote sensing satellite constellation /
- mission planning algorithms /
- benchmark framework

HTML全文

图 1 动态任务插入打分模型

下载: 全尺寸图片幻灯片

图 2 算法生成的卫星任务规划条带

下载: 全尺寸图片幻灯片

表 1 任务场景设置

难度	资源普查点	资源普查区域	追加点	追加区域	光学卫星	SAR卫星
低	45	5	5	1	20	10
中	180	20	20	5	52	28
高	450	50	50	10	132	68

下载: 导出CSV

表 2 测评数据核心数据表及其字段说明

数据表	字段	类型	说明
卫星星历	geometry	POINT	星下点经纬度(WGS84)
	time	INTEGER	从UTC0点开始的秒数
	altitude	REAL	卫星高度(km)
	sun_elevation	REAL	星下点太阳高度角(度)
点任务	mission_id	TEXT	任务唯一标识符
	geometry	POINT	任务地理位置
	priority	INTEGER	优先级(1-10)
	frequency	INTEGER	要求执行次数
	min_interval	REAL	最小观测间隔(小时)
	time_start	INTEGER	最早可执行时间(秒)
	time_end	INTEGER	最晚可执行时间(秒)
区域任务	mission_id	TEXT	任务唯一标识符
	geometry	POLYGON	区域边界
	area	REAL	区域面积 (km², EPSG:6933)
	coverage_ratio	REAL	要求覆盖率
	priority	INTEGER	优先级
	time_start	INTEGER	最早可执行时间(秒)
	time_end	INTEGER	最晚可执行时间(秒)
卫星	satellite_id	TEXT	卫星标识符
	type	TEXT	optical/SAR
	swath	REAL	幅宽(km)
	max_roll_left	REAL	最大左侧摆角(度)
	max_roll_right	REAL	最大右侧摆角(度)
	min_sun_elev	REAL	最小太阳高度角(光学)
	min_inc_angle	REAL	最小入射角(SAR)
	max_inc_angle	REAL	最大入射角(SAR)
	resolution	REAL	分辨率(m)
	min_on_time	REAL	单次最小开机时长(秒)
	max_on_time_orbit	REAL	单圈最大开机时长(秒)
	trans_time_0	REAL	0度侧摆机动时长(秒)
	trans_time_10	REAL	10度侧摆机动时长(秒)
	trans_time_20	REAL	20度侧摆机动时长(秒)

下载: 导出CSV

表 3 基线模型在不同难度场景下的任务完成率

模型	低难度 (L1)	中难度 (L2)	高难度 (L3)
随机调度	7.3%	3.8%	1.9%
优先级贪婪算法	76.1%	63.7%	49.2%

下载: 导出CSV

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