Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications, such as early warning and surveillance system, intelligent traffic system and so on. Through trajectory similarity measurement and clustering, target behavior patterns can be found from massive spatiotemporal trajectory data. In order to mine frequent behaviors of targets from complex historical trajectory data, a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper. Firstly, spatial–temporal Hausdorff distance is proposed to measure multidimensional information differences of spatiotemporal trajectories, which can distinguish the behaviors with similar location but different course and velocity. On this basis, by combining the idea of k-nearest neighbor and density peak clustering, a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribution. Finally, we implement the proposed algorithm in simulated and radar measured trajectory data respectively. The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately compared to the existing methods, which has a good application prospect in intelligent monitoring tasks.     

基于多模特征融合的雷达干扰信号识别

传统制导雷达面临的新型有源干扰样式越来越复杂，雷达必须对各种干扰类型加以鉴别。传统的干扰识别方法仅对特定单一样式有效，通用性较差、泛化能力较弱，无法应对复杂多变的干扰对抗环境。因此，必须提出智能化更高、稳健性更强的普适干扰识别方法，提升制导武器抗干扰能力。为了提高干扰信号识别的准确率，研究了多模特征融合算法，并最终对时域、时频域、信息论特征进行融合以实现分类。首次将信息论中熵、相对熵、相对距离等概念引入到干扰信号分类这个应用场景中，通过仿真实验表明，能够有效对常见干扰进行有效识别，在较低干噪比下也有较好的识别准确率。     

3D打印技术在空间站梦天实验舱上的应用

面对梦天实验舱次结构减重需求，提出了面向3D打印技术的次结构拓扑优化设计的方法，完成并掌握了3D打印次结构从设计、工艺、制造、检验等全周期的研制流程。在梦天实验舱发射载荷条件下，通过结构有限元分析及试验验证，对3D打印次结构性能进行了考核。结果表明:在具有一定强度和刚度裕度的前提下，支架减重效果较为明显，面向3D打印技术的次结构拓扑优化设计的方法具有广阔的工程应用前景。     

A clustering based method to complete frame of discernment

When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness.     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

卫星SAR载荷蓄电池充电器的建模分析与设计

随着现代商业卫星的发展，低成本、重量轻、多功能、高集成的微纳合成孔径雷达(SAR)卫星迎来加速发展的阶段。由于SAR载荷工作时间短、峰值功率大的特点，需专门为SAR载荷配置一组高压载荷蓄电池，在相同电流下，可输出更大功率。针对高压载荷蓄电池恒流恒压充电的需求，提出采用推挽变换器的隔离升压充电器方案，搭建了推挽变换器的小信号模型，采用峰值电流控制的双环控制策略实现恒流恒压充电控制，最后在充电器样机上进行了实验验证。实验结果验证了分析的正确性与设计的有效性，可以满足SAR载荷蓄电池充电的要求。     

基于自适应通信拓扑的无人机集群弱路径约束下的分布式控制器设计

针对传统无人机集群飞行控制需要规划过多飞行路径的问题，设计一种结合线性二次型调节器(LQR)与机间防撞的分布式协同控制器，不需要为集群设计相对构型，只需给定一条满足无人机动力学约束的飞行路径，集群就可沿路径飞行。该控制器通过设计自适应通信拓扑来稳定加入机间防撞机制带来的抖振效应，并使集群具有良好可扩展性。仿真结果表明，所设计的分布式控制器可以实现规划一条路径下无人机集群无机间碰撞的稳定飞行。     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

Timeliness optimization of unmanned aerial vehicle lossy communications for internet-of-things

Information freshness is a key factor for Internet-of-Things(Io T) to make appropriate decisions and operations. This paper proposes an analytical framework for evaluating the timeliness performance of the Io T system based on Unmanned Aerial Vehicle(UAV) lossy communications.The performance analysis consists of the outage probability analysis and the Age-of-Information(Ao I) analysis with outages. To begin with, we solve a lossy coding problem formulated from the UAV communication system, and d...     

A multi-satellite co-tracking method for a single space target based on adaptive distributed spherical simplex information-weighted consensus filter

A multi-satellite co-tracking method for a single non-cooperative target is proposed to extend the arc of observation as well as to improve the accuracy of tracking. Firstly, the motion of the target is considered to be affected by J2 perturbation, and the model of multi-satellite co-tracking a single space target is designed with only measured angles. Then, a multi-satellite co-tracking method for a single space target based on an adaptive distributed spherical simplex information-weighted consensus filter (ADSSICF) is proposed. The spherical simplex is used to reduce the computational cost of the information filter, and to improve the efficiency of tracking. The angle-only measurement equation is linearized by the statistical linear regression method, and linearization errors are compensated for by updating measurement noise. By designing an adaptive consensus algorithm, only a small number of iterations are needed to achieve network consensus, to improve the convergence speed of the consensus algorithm, and prove the stability of ADSSICF. Finally, a simulation of four low-orbit satellites tracking a single space target is established. The focus of this paper is on the real-time performance and tracking accuracy of the multi-satellite co-tracking a single space target with angle-only based on ADSSICF. The performance of ADSSICF is verified from three indicators: the consensus and convergence of algorithm, the accuracy of state estimation.