基于多智能体的多机型协同作战决策系统模型研究

多机型协同作战决策系统开发是一项很复杂的工程,各组成要素之间的活动很难进行定量分析和描述,战场情况也复杂多变,具有不可预见性和不可再现性。文中基于目前多机协同作战仿真的发展现状,运用多智能体(Multi-agent)技术,提出了多机型协同作战复杂系统分析与设计决策仿真模型;介绍了运用群建模和开发工具对基于Multi-agent的多机型协同作战决策系统仿真的实现。     

基于Swarm的任务分配系统模型的实现

随着计算机科学在人工智能领域的研究和发展,借助于计算机,人们即可以在Swarm平台上进行模拟仿真,从而对各类复杂系统进行可信赖的实验室研究。以现实生活中常常出现的任务分配这个复杂系统为例,详尽的描述和说明在Swarm平台上模拟实现该系统的全过程。     

飞行器集群协同制导控制方法及应用研究

分析了目前多飞行器协同作战的发展现状与趋势，指出了多飞行器以编队形式进行协调与配合，可以有效提高突防概率与作战效能。针对飞行器集群协同制导控制技术，分析梳理了相关的技术难点。然后，分别对协同任务规划与动态目标分配方法和多飞行器编队协同控制方法、多约束条件下的分布式协同制导方法的国内外研究现状进行了介绍。最后对飞行器集群协同制导控制技术进行了总结，并对未来的发展方向进行了展望。     

Thermosphere densities derived from Swarm GPS observations

After the detection of many anomalies in the Swarm accelerometer data, an alternative method has been developed to determine thermospheric densities for the three-satellite mission. Using a precise orbit determination approach, non-gravitational and aerodynamic-only accelerations are estimated from the high-quality Swarm GPS data. The GPS-derived non-gravitational accelerations serve as a baseline for the correction of the Swarm-C along-track accelerometer data. The aerodynamic accelerations are converted directly into thermospheric densities for all Swarm satellites, albeit at a much lower temporal resolution than the accelerometers would have been able to deliver. The resulting density and acceleration data sets are part of the European Space Agency Level 2 Swarm products.To improve the Swarm densities, two modifications have recently been added to our original processing scheme. They consist of a more refined handling of radiation pressure accelerations and the use of a high-fidelity satellite geometry and improved aerodynamic model. These modifications lead to a better agreement between estimated Swarm densities and NRLMSISE-00 model densities. The GPS-derived Swarm densities show variations due to solar and geomagnetic activity, as well as seasonal, latitudinal and diurnal variations. For low solar activity, however, the aerodynamic signal experienced by the Swarm satellites is very small, and therefore it is more difficult to accurately resolve latitudinal density variability using GPS data, especially for the higher-flying Swarm-B satellite. Therefore, mean orbit densities are also included in the Swarm density product.     

一种空间邻近目标红外多传感器立体分辨方法

空间邻近目标在红外像平面的成像因相互交叠而形成簇状像斑,对红外传感器的信号处理提出了分辨的要求。为实现对空间邻近目标的立体跟踪定位,提出基于量子粒子群优化的空间邻近目标红外多传感器立体分辨方法。在对目标像平面成像建模基础上,构建基于最小二乘准则的空间邻近目标立体分辨目标函数,针对目标函数的高维非线性特点,以QP-SO解决目标函数优化问题,估计目标空间位置。仿真结果表明:相比于传统的先单传感器像平面分辨后多传感器视线交叉定位方法,此法具有更优的目标位置估计精度、辐射强度估计精度和目标个数估计正确率。     

基于粒子群优化的光伏阵列最大功率点跟踪法研究

光伏阵列在局部阴影条件下,出现反向雪崩效应,导致输出功率出现多个局部极大值点,此时常规的最大功率点跟踪（MPPT）失效。基于比较完善的光伏电池遮挡模型,对光伏阵列进行建模,验证其输出功率的多峰特性。提出一种基于粒子群优化算法（PSO）的多峰最大功率点跟踪方法,并积累粒子个体搜索经验,提高跟踪速度。仿真表明,当外界条件变化时,此方法可以快速跟踪光伏模块的最大功率,从而有效地利用能源。     

Differential code bias estimation based on uncombined PPP with LEO onboard GPS observations

Differential Code Bias (DCB) is an essential correction that must be provided to the Global Navigation Satellite System (GNSS) users for precise position determination. With the continuous deployment of Low Earth Orbit (LEO) satellites, DCB estimation using observations from GNSS receivers onboard the LEO satellites is drawing increasing interests in order to meet the growing demands on high-quality DCB products from LEO-based applications, such as LEO-based GNSS signal augmentation and space weather research. Previous studies on LEO-based DCB estimation are usually using the geometry-free combination of GNSS observations, and it may suffer from significant leveling errors due to non-zero mean of multipath errors and short-term variations of receiver code and phase biases. In this study, we utilize the uncombined Precise Point Positioning (PPP) model for LEO DCB estimation. The models for uncombined PPP-based LEO DCB estimation are presented and GPS observations acquired from receivers onboard three identical Swarm satellites from February 1 to 28, 2019 are used for the validation. The results show that the average Root Mean Square errors (RMS) of the GPS satellite DCBs estimated with onboard data from each of the three Swarm satellites using the uncombined PPP model are less than 0.18 ns when compared to the GPS satellite DCBs obtained from IGS final daily Global Ionospheric Map (GIM) products. Meanwhile, the corresponding average RMS of GPS satellite DCBs estimated with the conventional geometry-free model are 0.290, 0.210, 0.281 ns, respectively, which are significantly larger than those obtained with the uncombined PPP model. It is also noted that the estimated GPS satellite DCBs by Swarm A and C satellites are highly correlated, likely attributed to their similar orbit type and space environment. On the other hand, the Swarm receiver DCBs estimated with uncombined PPP model, with Standard Deviation (STD) of 0.065, 0.037 and 0.071 ns, are more stable than those obtained from the official Swarm Level 2 products with corresponding STD values of 0.115, 0.101, and 0.109 ns, respectively. The above indicates that high-quality DCB products can be estimated based on uncombined PPP with LEO onboard observations.     

Decentralized formation pose estimation for spacecraft swarms

For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltech’s robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm.     

Mission-driven autonomous perception and fusion based on UAV swarm

Distributed autonomous situational awareness is one of the most important foundation for Unmanned Aerial Vehicle (UAV) swarm to implement various missions. Considering the application environment being usually characterized by strong confrontation, high dynamics, and deep uncertainty, the distributed situational awareness system based on UAV swarm needs to be driven by the mission requirements, while each node in the network can autonomously avoid collisions and perform detection mission through limited resource sharing as well as complementarity of respective advantages. By efficiently solving the problems of self-avoidance, autonomous flocking and splitting, joint estimation and control, etc., perception data from multi-platform multi-source should be extracted and fused reasonably, to generate refined, tailored target information and provide reliable support for decision-making.     

Decentralized differential drag based control of nanosatellites swarm spatial distribution using magnetorquers

Nanosatellites in the swarm initially move along arbitrary unbounded relative trajectories according to the launch initial conditions. Control algorithms developed in the paper are aimed to achieve the required spatial distribution of satellites in the along-track direction. The paper considers a swarm of 3U CubeSats in LEO, their form-factor is suitable for the aerodynamic control since the ratio of the satellite maximum to minimum cross-section areas is 3. Each satellite is provided with the information about the relative motion of neighboring satellites inside a specified communication area. The paper develops the corresponding decentralized control algorithms using the differential drag force. The required attitude control for each satellite is implemented by the active magnetic attitude control system. A set of decentralized control strategies is proposed taking into account the communicational constraints. The performance of these strategies is studied numerically. The swarm separation effect is demonstrated and investigated.