PDBSCAN: Parallel DBSCAN for Large-Scale Clustering Applications

Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.

Principal component analysis and cluster analysis based orbit optimization for earth observation satellites

This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.

Semi-supervised Affinity Propagation Clustering Based on Subtractive Clustering for Large-Scale Data Sets

In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.

Agile Satellite Mission Planning via Task Clustering and Double-Layer Tabu Algorithm

Satellite observation schedule is investigated in this paper.A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite.The newly developed method can make the satellite observe more targets and therefore save observation resources.First,for the densely distributed target points,a preprocessing scheme based on task clustering is proposed.The target points are clustered according to the distance condition.Second,the local observation path is generated by Tabu algorithm in the inner layer of cluster regions.Third,considering the scatter and cluster sets,the global observation path is obtained by adopting Tabu algorithm in the outer layer.Simulation results show that the algorithm can effectively reduce the task planning time of large-scale point targets while ensuring the optimal solution quality.

Performance Analysis of Spectrum Sensing Based on Distributed Satellite Clusters under Perturbation

In this paper,we investigate the spectrum sensing performance of a distributed satellite clusters(DSC)under perturbation,aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals.Specifically,we propose a cooperative beamforming(BF)algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation.Then,a constrained optimization problem with maximizing the signal to interference plus noise ratio(SINR)is modeled to obtain the BF weight vectors,and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector.Finally,we derive the closedform expression of the detection probability for the considered DSC over Shadowed-Rician fading channels.Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.

Inter-satellite Link Topology Design and Relative Navigation for Satellite Clusters

A distributed relative navigation approach via inter-satellite sensing and communication for satellite clusters is proposed. The inter-satellite link(ISL)is used for ranging and exchanging data for the relative navigation,which can improve the autonomy of the satellite cluster. The ISL topology design problem is formulated as a multi-objective optimization problem where the energy consumption and the navigation performance are considered. Further,the relative navigation is performed in a distributed fashion,where each satellite in the cluster makes observations and communicates with its neighbors via the ISL locally such that the transmission consumption and the computational complexity for the navigation are reduced. The ISL topology optimization problem is solved via the NSGA-Ⅱ algorithm,and the consensus Kalman filter is used for the distributed relative navigation. The proposed approach is flexible to varying tasks,with satellites joining or leaving the cluster anytime,and is robust to the failure of an individual satellite. Numerical simulations are presented to verify the feasibility of the proposed approach.

Approach to inter-satellite time synchronization for micro-satellite cluster

Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster is important for both cluster sensing capabilities and its autonomous operating. However, the existing time synchronization methods are not suitable for microsatellite cluster, because it requires too many human interventions and occupies too much ground control resource. Although, data post-process may realize the equivalent time synchronization, it requires processing time and powerful computing ability on the ground, which cannot be implemented by cluster itself. In order to autonomously establish and maintain the time benchmark in a cluster, we propose a compact time difference compensation system（TDCS）, which is a kind of time control loop that dynamically adjusts the satellite reference frequency according to the time difference. Consequently, the time synchronization in the cluster can be autonomously achieved on-orbit by synchronizing the clock of other satellites to a chosen one＇s. The experimental result shows that the standard deviation of time synchronization is about 102 ps when the carrier to noise ratio（CNR） is 95 d BHz, and the standard deviation of corresponding frequency difference is approximately0.36 Hz.

Approach to MAI cancellation for micro-satellite clusters

With the development of micro-satellite technology,traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability.For satellite clusters based on the frequency division-code division multiple access(FD-CDMA)communication system,the inter-satellite ranging precision is usually constrained due to the influence ofmulti-address interference(MAI).Themulti-user detection(MUD)is a solution to MAI,which can be divided into two categories:the linear detector(LD)and the non-linear detector(NLD).The general idea of the LD is aiming to make a better decision during the symbol decision process by using the information of all channels.However,it is not beneficial for the signal phase tracking precision.Instead,the principle of the NLD is to rebuild the interference signal and cancel it from the original one,which can improve the ranging performance at the expense of considerable delays.In order to enable simultaneous ranging and communication and reduce multi-node ranging performance degradation,this paper proposes an NLD scheme based on a delay locked loop(DLL),which simplifies the receiver structure and introduces no delay in the decision process.This scheme utilizes the information obtained from the interference channel to reconstruct the interference signal and then cancels it from the original delayed signal.Therefore,the DLL input signal-to-interference ratio(SIR)of the desired channel can be significantly improved.The experimental results show that with the proposed scheme,the standard deviation of the tracking steady error is decreased from 5.59 cm to 3.97 cm for SIR=5 dB,and 13.53 cm to 5.77 cm for SIR=-5 dB,respectively.

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).

A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

In this paper,the clustering analysis is applied to the satellite image segmentation,and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power.The method firstly adopts the fuzzy C-means clustering(FCM)to obtain the satellite cloud image segmentation.Secondly,in the cloud image,we dispose the‘high-density connected’pixels in the same cloud clusters and the‘low-density connected’pixels in different cloud clusters.Therefore,we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge.Finally,using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters,thunderstorm cloud clusters are quickly and accurately identified.The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.

EQUIVALENT BASELINE AND INTERFEROMETRIC PHASE OF CLUSTER SATELLITE SAR

The change of the equivalent baseline and interferometric phase of cluster SAR satellites is analyzed when the constellation circles around the Earth and the satellites rotate around the center at the same time. The letter provides assessment of baseline error and phase error which influence the precision of height measurement in the across-track interferometric mode. The mathematical model of cluster satellite movement is built, simulation analyses and the curve of height error are presented. The simulation results show that height measurement error can be compensated by the formulae derived in this letter, therefore, the Digital Elevation Models (DEM’s) are recovered accurately.

On the Large-Scale Structure of the Universe as given by the Voronoi Diagrams

The size distributions of 2D and 3D Voronoi cells and of cells of Vp（2, 3）,--2D cut of 3D Voronoi diagram--are explored, with the slngle-parameter （re-scaled） gamma distribution playing a central role in the analytical fitting. Observational evidence for a cellular universe is briefly reviewed. A simulated Vp（2, 3） map with galaxies lying on the cell boundaries is constructed to compare, as regards general appearance, with the observed CfA map of galaxies and voids, the parameters of the simulation being so chosen as to reproduce the largest observed void size.

Distributed Satellite Cluster Network:a Survey

The ability of the monolithic satellite,satellite orbit(especially GEO),and radio resource are very limited,so the development of distributed satellite cluster network(DSCN) receives more and more worldwide attention.In this paper,DSCN is surveyed and the study status of DSCN architecture design is summarized.The formation flying of spacecrafts,reconfiguration,networking,and applied research on distributed satellite spacecraft are described in detail.The DSCN will provide a great technology innovation for space information network,satellite communications,satellite navigation,deep space exploration,and space remote sensing.In addition,this paper points out future trends of the DSCN development.

卫星互联网支撑跨域无人集群作战

跨域无人集群作为一种颠覆性的未来全新作战模式,其作战效能的发挥极大地受限于通信组网能力。近年来,卫星互联网凭借其广覆盖、低延时、高宽带及低成本等技术优势,在全球快速发展,是进一步发展跨域无人集群作战模式的关键。文中针对未来水下无人系统的发展趋势和现实需求,介绍了卫星互联网的发展、技术优势以及跨域无人集群的作战概念,深入分析了卫星互联网支撑跨域无人集群作战的优势,提出了结合卫星互联网技术与空域、海面、水下作战域无人集群相结合的空海跨域无人集群作战网络,并综合分析给出了技术架构,可为未来跨域无人集群作战研究提供参考。

分布式星群中的协同计算卸载与资源分配

在分布式星群在轨建造、维修的全天基场景中,星群动态性大,时延敏感类(A类)与时延容忍类(B类)任务由资源受限的终端卫星并发产生,而传统的天地协同卸载场景中不考虑卫星动态性和任务类型的多样性。针对全天基场景的需求,构建了一种分布式星群边缘计算架构,提出了一种差分自适应奖励系统DDPG(DARS-DDPG)算法。通过将差分自适应奖励系统引入传统DDPG算法,使算法在学习过程中能够区分出不同类型任务的重要性,并自适应调整两类任务的惩罚系数,使两类任务的完成率达到最高。仿真表明,DARS-DDPG学习出的策略相较于基线策略以及传统DDPG学习出的策略,在任务时延以及A、B类任务完成率上都有大幅提升。

应用动态感知的卫星分布式集群管理平台设计与实现

卫星互联网环境中存在卫星节点资源受限、链路拓扑高动态变化和网络传输高时延等问题。针对上述问题导致的卫星节点资源利用率低、系统服务响应能力较弱、负载不均衡,提出利用容器编排技术整合卫星节点资源的卫星分布式集群管理平台构建方案。其中,重点针对Kubernetes默认资源编排调度策略无法适用于卫星互联网复杂链路环境的问题,设计一种应用动态感知的卫星分布式集群资源调度方法,通过分布式集群资源状态实时感知模块,采集卫星节点CPU、内存、磁盘及网络资源的负载情况作为评价指标,完成卫星互联网环境下不同应用Pod资源对象的优化调度。仿真结果表明:应用动态感知调度方法构建的卫星分布式集群管理平台,可以实现卫星网络环境下系统资源的合理调度,提升系统负载均衡能力,进而保证系统服务的快速响应。

卫星通信中基于流量均衡的跳波束分簇构型研究

星上资源有限且珍贵,而在真实的通信场景中,地面终端用户地理位置分布和卫星通信业务需求各不相同,因此需要进行动态星上资源管理,并设计灵活高效的资源管理方案。本文利用跳波束技术提供波束级别灵活的资源管理,解决高通量卫星产生的点波束覆盖地面小区之间业务需求量不均衡的问题。首先,针对跳波束卫星存在共信道干扰的问题,提出了基于频率模式切换的跳波束簇间干扰避免策略;然后,提出均匀分簇和非均匀分簇两种分簇构型策略,针对均匀分簇均衡效果差、不适用于地面动态变化的问题和共信道干扰问题,提出了基于频率模式切换的跳波束簇间干扰避免策略的非均匀分簇构型方案;最后,对干扰避免策略进行仿真,同时基于多种智能优化算法对不同的分簇策略进行验证,证明所提分簇构型的可行性,并表明无距离限制的非均匀分簇算法的流量均衡能力较传统分簇算法更强。

Distributed adaptive prescribed-time orbit containment control for satellite cluster flight

The distributed prescribed-time orbit containment control for the satellite cluster flight with multiple dynamic leaders is investigated.The directed information communication topology between followers is taken into account in the overall paper.When the satellite mass is assumed to be constant,a distributed prescribed-time orbit containment controller is,firstly,presented to drive the followers into the dynamic convex hull produced by multiple leaders.Then,the parameter uncertainty is considered,and a prescribed-time sliding mode estimator is introduced to estimate the desired velocity of each follower.Based on the estimated state,a novel distributed adaptive prescribed-time orbit containment control scheme is proposed.The Lyapunov stability theory is utilized to prove the prescribed-time stability of the closed-loop system.Finally,several numerical simulations and comparison of different control methods are provided to verify the effectiveness and superiority of the proposed control method.

基于DBSCAN聚类算法的卫星数据分区异常检测

随着我国第一颗电磁监测卫星的发射,卫星探测的海量数据不断涌现,探究空间载荷数据变化特征已成为当前研究热点。为了对张衡一号卫星LAP载荷数据进行异常检测,该文将处理后的数据按地理纬度划分为南纬50°到南纬20°,南纬20°到北纬20°,北纬20°到北纬50°三个区域,依次采用DBSCAN密度聚类算法进行聚类异常检测。结果表明,该方法可用于对LAP数据的异常检测。DBSCAN密度聚类算法可用于检测卫星异常数据,为检测卫星探测数据异常、研究空间数据变化特征提供思路参考。