面向移动目标的成像卫星协同任务规划综述

徐卓; 樊盛华; 岳海涛; 瞿涛; 汪鼎文; 孙世磊

doi:10.11999/JEIT260133

面向移动目标的成像卫星协同任务规划综述

doi: 10.11999/JEIT260133 cstr: 32379.14.JEIT260133

1.
武汉大学计算机学院武汉 430072
2.
中国科学院空天信息创新研究院北京 100094

详细信息

作者简介:
徐卓：男，博士生，研究方向为卫星任务规划、智能优化与调度

樊盛华：男，博士后，研究方向为卫星任务规划、遥感图像处理

岳海涛：男，助理研究员，研究方向为信号与信息处理

瞿涛：男，副教授，研究方向为卫星任务规划、遥感图像目标检测

汪鼎文：男，教授，研究方向为卫星任务规划、时空信息智能处理

孙世磊：男，研究员，研究方向为卫星任务规划、遥感图像智能解译

通讯作者:
孙世磊　sunsl@whu.edu.cn

中图分类号: TP391; TP79
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- 被引次数: 0
出版历程
- 修回日期: 2026-06-29
- 录用日期: 2026-06-29
- 网络出版日期: 2026-07-07

A Survey of Cooperative Mission Planning for Imaging Satellites Observing Moving Targets

1.
School of Computer Science, Wuhan University, Wuhan 430072, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

摘要

摘要: 面向移动目标的成像卫星协同任务规划是实现天基对地观测系统从“静态区域覆盖”向“广域搜索—动态跟踪—反馈补搜”动态闭环转型的关键技术，在应急响应、海洋监测、广域态势感知与重点目标持续观测等领域具有重要意义。其核心挑战源于移动目标未来状态的不确定性与传统任务规划模型对确定性输入依赖之间的矛盾。该文从不确定性驱动视角出发，结合海面、空天、地面三类典型移动目标的运动特征与观测需求，系统梳理该领域近年来的主要技术进展。重点剖析运动状态预测与时空不确定性表征、多星协同观测任务规划及闭环重规划等关键环节，对比模型驱动与数据驱动预测方法在不确定性表征和规划可用性的差异，分析集中式、分布式与混合式架构的算法选择逻辑与动态响应能力。最后，该文指出该领域正由开环静态规划向闭环动态调度转型，总结预测不确定性信息在规划决策中利用不足、分布式协同中状态认知不一致等关键瓶颈，并展望了跨层级耦合建模、大语言模型赋能的智能优化等未来发展方向。
- 卫星任务规划 /
- 多星协同 /
- 移动目标 /
- 轨迹预测 /
- 在轨智能
Abstract: Significance Cooperative mission planning of imaging satellites for moving targets is a key technique for supporting the transition of space-based Earth observation systems from static regional coverage to a dynamic closed-loop mode involving wide-area search, dynamic tracking, and feedback-driven search refinement. It is important for emergency response, maritime monitoring, wide-area situational awareness, and persistent observation of key moving targets. The central difficulty lies in the conflict between the uncertainty of future target states and the dependence of conventional mission planning models on deterministic inputs. Moving targets do not correspond to fixed locations or fixed visibility windows. Their future states usually evolve as probability distributions, confidence regions, or grid-based existence probabilities. Effective planning therefore requires not only predicting target motion, but also transforming uncertainty information into planning objectives, constraints, and replanning triggers. A systematic review from an uncertainty-driven perspective on moving-target observation is therefore still needed. Progress This review summarizes cooperative mission planning of imaging satellites for moving targets from an uncertainty-driven perspective. Typical moving targets are classified into maritime targets, highly time-sensitive aerospace targets, and ground moving targets according to their operating domains, dynamic characteristics, and observation requirements. Although these targets differ in maneuverability, prior constraints, and observation windows, they share a common planning problem: uncertain target motion must be coupled with deterministic satellite observation actions under strict platform and resource constraints. Target motion prediction and spatiotemporal uncertainty representation methods are reviewed. Physics-based methods describe target state evolution through kinematic and dynamic constraints, covariance propagation, reachable regions, or Markov transition models. Data-driven methods learn motion patterns from historical trajectories, Automatic Identification System data, remote sensing observations, meteorological factors, and geographic constraints. From the planning perspective, the value of these methods depends on whether their outputs, such as covariance, existence probability, confidence region, or information gain, can be directly used by planning models. Observation task modeling, cooperative architectures, optimization algorithms, and closed-loop replanning mechanisms are further analyzed. Deterministic simplification models reduce uncertainty into trajectory points, visibility windows, or fixed geographic regions, while probabilistic task modeling incorporates target existence probability, belief state, or information gain into objective functions, constraints, and state transitions. Centralized, distributed, and hybrid architectures are compared in terms of global coordination, onboard autonomy, communication cost, and response timeliness. Exact methods, heuristic rules, metaheuristics, deep reinforcement learning, and large language model (LLM)-enabled solution assistance and algorithm design are discussed. State-triggered replanning, receding horizon optimization, and model predictive control are reviewed as representative mechanisms for closed-loop dynamic scheduling. Conclusions The reviewed studies show that the field is moving from open-loop static scheduling toward closed-loop dynamic planning. However, several bottlenecks remain. First, uncertainty information generated by the prediction layer is not sufficiently used in planning decisions. Rich probabilistic information is often compressed into deterministic time windows, discrete trajectory points, or geometric regions, which weakens risk-sensitive task allocation. Second, distributed cooperation still lacks reliable belief-state consistency. Local observations may lead different satellites to hold inconsistent beliefs about the same target state, causing redundant observations, task conflicts, or unbalanced resource use. Third, dynamic replanning lacks unified benefit-cost criteria for triggering replanning. Frequent replanning may consume excessive attitude maneuvering time, energy, and storage resources, while delayed replanning may miss real target maneuvers. Fourth, LLMs have begun to support task requirement parsing, constraint modeling, heuristic generation, and algorithm design for satellite scheduling, but their use for moving target cooperative planning remains insufficient. Prospects Future research should focus on building a stronger closed-loop planning framework. Prediction uncertainty should be directly used in planning models through chance-constrained planning, belief-state planning, or partially observable Markov decision processes, so that covariance, existence probability, and information entropy can be incorporated into objectives, constraints, and replanning triggers. Bayesian updating or sequential filtering can feed both successful detections and missed detections back to the prediction layer. Lightweight state synchronization and belief fusion are needed for distributed cooperation, supported by compact state-sharing indicators and event-triggered communication. Replanning decisions should be guided by information gain and cost evaluation. Finally, LLMs should be developed as verifiable auxiliary tools rather than direct replacements for optimization solvers. They can support requirement structuring, constraint modeling, heuristic generation, and algorithm component design, while feasibility repair and performance evaluation should be completed by formal checkers, traditional optimizers, and simulation environments. These directions are expected to support robust and uncertainty-aware mission planning for satellite-based moving-target observation.
- Satellite mission planning /
- Multi-satellite collaboration /
- Moving targets /
- Trajectory prediction /
- Onboard intelligence

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图 1 物理机理主导的显式不确定性建模

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图 2 多星协同任务规划架构

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图 3 状态触发式重规划与滚动时域优化流程对比

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表 1 常见优化目标及说明

类型	优化目标	说明
观测效能 (最大化)	目标跟踪时长^[15]	延长对目标的连续监视时间
	目标发现/捕获概率	提升对潜在区域联合覆盖概率之和
	目标捕获次数^[16]	提高固定时间内对目标的捕获次数
信息价值 (最大化)	信息增益^[17](信息熵)	降低系统熵值，优先观测能显著降低目标状态不确定性的区域
信息价值 (最大化)	加权观测收益	考虑目标的战略价值(优先级)与观测紧迫度，实现全局任务收益最大化
系统代价 (最小化)	能源消耗	减少大角度姿态机动以降低稳定时间与能量消耗，同时均衡星上存储资源的占用

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表 2 面向移动目标的运动状态预测方法对比

预测范式	不确定性表征粒度	代表文献	主要算法与模型	不确定性表征能力
模型驱动	参数化连续演化	[20–26]	联合高斯分布、协方差椭圆	连续解析表征，低开销、强可解释性
模型驱动	非参数化离散转移	[17,27,28]	马尔可夫概率网格、状态转移矩阵	离散网格表征，适应复杂约束场景
数据驱动	隐式概率映射	[11,12,29–31]	LSTM、CNN多源特征融合	多粒度表征，强非线性拟合能力

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表 3 集中式优化方法对比分析

算法范式	代表文献	求解机制	核心优势	局限性	典型适用场景
精确方法	[13]	数学规划完备搜索	最优性可证、结果可靠	复杂度高、规模受限，对动态不确定性无容错能力	小规模问题理论验证与算法基准测试
启发式规则	[28,44–48]	领域规则贪婪构造	低开销、强实时性、易部署	依赖人工经验、易局部最优，对目标机动不确定性适应差	强实时性要求的快速规划场景
元启发式算法	[21,22,24,26,35, 38–43,49–54]	种群迭代、领域搜索、混合/超启发式	全局寻优强适配非线性约束	收敛慢、动态响应能力弱，对突发不确定性动态响应不足	多约束多目标的全局优化规划场景
深度强化学习	[14,34,36,55–57]	离线训练，在线端到端策略推理	毫秒级响应、环境自适应能力强	数据需求大、可解释性差	高动态不确定性下自主在线规划场景
LLM赋能求解	[58–61]	生成规则、算子或代码，反馈迭代优化	知识融合强，算法组合灵活	可行性、可重复性和部署成本待验证	离线算法设计在线辅助求解

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