Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Distributed cooperative task planning algorithm for multiple satellites in delayed communication environment

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.

Reactive scheduling of multiple EOSs under cloud uncertainties:model and algorithms

Most earth observation satellites(EOSs)are low-orbit satellites equipped with optical sensors that cannot see through clouds.Hence,cloud coverage,high dynamics,and cloud uncertainties are important issues in the scheduling of EOSs.The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed.Numerous studies have been conducted on methods for the proactive scheduling of EOSs,including expectation,chance-constrained,and robust optimization models and the relevant solution algorithms.This study focuses on the reactive scheduling of EOSs under cloud uncertainties.First,using an example,we describe the reactive scheduling problem in detail,clarifying its significance and key issues.Considering the two key objectives of observation profits and scheduling stability,we construct a multi-objective optimization mathematical model.Then,we obtain the possible disruptions of EOS scheduling during execution under cloud uncertainties,adopting an event-driven policy for the reactive scheduling.For the different disruptions,different reactive scheduling algorithms are designed.Finally,numerous simulation experiments are conducted to verify the feasibility and effectiveness of the proposed reactive scheduling algorithms.The experimental results show that the reactive scheduling algorithms can both improve observation profits and reduce system perturbations.

Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling problem.Based on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the population.The results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

General modeling and optimization technique for real-world earth observation satellite scheduling

Over the last two decades,many modeling and optimization techniques have been developed for earth observation satellite(EOS)scheduling problems,but few of them show good generality to be engineered in realworld applications.This study proposes a general modeling and optimization technique for common and real-world EOS scheduling cases;it includes a decoupled framework,a general modeling method,and an easy-to-use algorithm library.In this technique,a framework that decouples the modeling,constraints,and optimization of EOS scheduling problems is built.With this framework,the EOS scheduling problems are appropriately modeled in a general manner,where the executable opportunity,another format of the well-known visible time window per EOS operation,is viewed as a selectable resource to be optimized.On this basis,10 types of optimization algorithms,such as Tabu search and genetic algorithm,and a parallel competitive memetic algorithm,are developed.For simplified EOS scheduling problems,the proposed technique shows better performance in applicability and effectiveness than the state-of-the-art algorithms.In addition,a complicatedly constrained real-world benchmark exampled by a four-EOS Chinese commercial constellation is provided,and the technique is qualified and outperforms the in-use scheduling system by more than 50%.

Current and near-term advances in Earth observation for ecological applications

There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth’s biosphere at scales from local plots to the whole planet.We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s,were recently launched,or are planned for launch in this decade.We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies.We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum.The suite of instruments we describe measure land surface characteristics,including land cover,but provide a more detailed monitoring of ecosystems,plant communities,and even some species then possible from historic sensors.The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area(LMA),total nitrogen content,and leaf area index(LAI).They provide new information on physiological processes related to photosynthesis,transpiration and respiration,and stress detection,including capabilities to measure key plant and soil biophysical properties.These include canopy and soil temperature and emissivity,chlorophyll fluorescence,and biogeochemical contents like photosynthetic pigments(e.g.,chlorophylls,carotenoids,and phycobiliproteins from cyanobacteria),water,cellulose,lignin,and nitrogen in foliar proteins.These data will enable us to quantify and characterize various soil properties such as iron content,several types of soil clays,organic matter,and other components.Most of these satellites are in low Earth orbit(LEO),but we include a few in geostationary orbit(GEO)because of their potential to measure plant physiological traits over diurnal periods,improving estimates of water and carbon budgets.We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography,changes in surface structure,and 3-dimensional canopy properties such as height,area,vertical profiles,and gap structure.We provide a description of each instrument and tables to summarize their characteristics.Lastly,we suggest instrument synergies that are likely to yield improved results when data are combined.

Agile Earth Observation Satellite Scheduling Algorithm for Emergency Tasks Based on Multiple Strategies

During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing schemes.Thus,rapid satellite scheduling is urgently needed.As a new generation of three degree-of-freedom(roll,pitch,and yaw)satellites,agile earth observation satellites(AEOSs)have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time windows.Thus,they are very suitable for emergency tasks.However,current task scheduling models and algorithms ignore the time,storage and energy consumed by pitch.Thus,these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency tasks.In this study,we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency tasks.First,a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time windows.Second,a model that considers flexible pitch and roll capabilities is designed.Finally,a scheduling algorithm based on merging insertion,direct insertion,shifting insertion,deleting insertion,and reinsertion strategies is proposed to solve conflicting problems quickly.To verify the effectiveness of the algorithm,48 groups of comparative experiments are carried out.The experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the scheme.Furthermore,the proposed method can support hybrid satellite resource scheduling for emergency tasks.

Optimization of earth observation satellite system based on parallel systems and computational experiments

Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experi- ments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.

Land Monitoring Network Services based on international geospatial standards:SOSI and geoland2/SDI Projects

Spatial Observation Services and Infrastructure is a project to develop and verify innovative infrastructure and services within the context of land monitoring and Earth Observation initiatives at European and Member State(MS)levels.The project’s results contribute to the concept definition of the Shared Environmental Information System(SEIS)emphasising symmetrical online sharing of information as opposed to unidirectional reporting.In a pre-operational set-up,involving the European Environment Agency(EEA),four MS sites and the European Space Agency(ESA),a decentralised information system respecting the Infrastructure for Spatial Information in Europe(INSPIRE)principles is demonstrated integrating distributed data and processing services as well as interactive multi-lingual access.The geoland2 project is carried out in the context of Global Monitoring for the Environment and Security(GMES),a joint initiative of the European Commission and ESA,aiming to build up a European capacity for GMES.The Spatial Data Infrastructure(SDI)task shall set up operational services for the discovery,viewing,access and delivery of all products generated in the geoland2 project.The paper presents the projects along the five International Standards Organisation(ISO)/Reference Model of Open Distributed Processing viewpoints and concludes with the main recommendation that service provisioning can strongly benefit from a(re-)utilisation of the Service Support Environment(SSE)technology provided sustainably by ESA and from the related rich experience.

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes.This gives rise to the Multisatellite Multimode Crosslink Scheduling(MMCS)problem,which involves allocating observation requests to agile satellites,selecting appropriate timing and observation modes for the requests,and transmitting the data to the ground station via the satellite communication system.Herein,a mixed integer programming model is introduced to include all complex time and operation constraints.To solve the MMCS problem,a two-stage heuristic method,called Fast insertion Tabu Search with Conflict-avoidance(FTS-C)heuristic,is developed.In the first stage,a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download.Further,the tabu search-based second stage optimizes the initial solution.Finally,an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.