一种飞-续-飞随机谱的编制方法及软件

本文介绍了一种飞-续-飞随机谱的编制方法和软件。它以空测的重心过载数据为基础,经过数据处理后,建立对应于每个飞行任务的Markov矩阵。并调整统计平均后的Markov矩阵以保证可得到一个不中断的过载序列。然后从矩阵中抽取半循环序列,构成飞-续-飞随机谱。本文中讨论了矩阵中半循环的随机抽取方法,给出了应用实例。     

空海自组网随机接入仿真研究

针对无人机在远海远域协同任务过程中的空海信息协同共享需求,提出了一种基于优先级传输的动态随机接入协议。首先,通过设计高效集群组网信令保证集群测控通信自组网的遥测、遥控信息的稳定传输;然后,结合改进的TDMA （时分多址）组网时隙结构实现节点快速入网与动态时隙分配协议;最后,通过OPNET （网络仿真技术软件包）分析所设计的集群空海自组网系统的随机接入低时延、业务传输高吞吐量和端到端传输低时延性能。结果表明：该组网信令下建网时间只需要2～3个飞控周期,在32节点场景下节点完成随机Aloha入网时延平均不到0.6 s,改进的TDMA协议在端到端时延比传统TDMA协议降低1/3。     

基于非导航随机信号的组合导航技术研究

目前,卫星导航系统已成为无处不在的导航和授时信息源,但是其自身存在固有的缺陷,如信号功率低、易被敌方压制或欺骗等。为了弥补卫星导航系统的不足,开展了基于地面数字电视广播(DTMB)和接收信号强度指示(RSSI)移动终端等非导航随机信号的导航信息提取技术研究和组合导航技术研究。根据DTMB时域PN帧头序列的良好时域相关性,联合频域传输信令,完成了数字电视信号的高精度测距;根据RSSI移动终端的无线信号传播模型和代表性实测数据,通过线性回归等数学手段对距离衰减公式进行优化,以完成移动终端相对基站的精确距离测量;根据非导航随机信号提取的导航信息,完成基于非导航随机信号的组合导航技术研究。经理论研究和试验验证,通过DTMB和RSSI移动终端等非导航随机信号可以提取高精度导航信息,组合导航技术研究能够满足高精度导航定位的应用需求,具有良好的工程应用价值。     

结构参数对弹上电子设备双层减振系统 减振性能的影响规律研究

针对弹上电子设备双层减振系统结构参数设计问题,研究了一二级减振系统的频率比、质量比、阻尼比对双层减振系统在随机振动激励下的绝对加速度响应峰值、全频段的加速度响应均方根值、高频段的加速度响应均方根值、相对位移响应均方根值、耦合频率等动力学参数的影响规律,得出一般性的结论,并探讨了双层减振技术的应用,可为弹上电子设备双层减振系统的减振性能设计提供理论支撑。     

相关随机序列仿真的一种新方法

相关随机序列是描述随机噪声、杂波和干扰的主要工具,在卫星通信、常规雷达及星载雷达中有着重要应用.本文基于“随机向量抽样法“,提出了一种新的相关随机序列仿真方法.这种方法通过由相关函数抽样序列形成的协方差矩阵控制序列的相关性,适用于仿真具有不同概率密度函数的各种有限长相关的随机序列.仿真结果表明该方法可快速、准确地实现各种相关随机序列的仿真.     

Multi-seasonal evaluation of hybrid poplar (P. Deltoides) plantations using Worldview-3 imagery and State-Of-The-Art ensemble learning algorithms

Forest resources are the primary components of the ecosystem environment. Poplars (Populus sp.), a member of the fast-growing trees, are one of the most productive forest tree species for industrial production thanks to their desirable traits comprising rapid growth, hybridization ability, and ease of propagation. Determining poplar cultivated areas and mapping their geographical distributions is critical for planners and decision-makers to increase the ecological and economic benefits of poplar trees. Due to the biodiversity of each geographical region and seasonal vegetation variations, classification based on remotely sensed imagery is essential for cropland monitoring. The main goal of this study is to evaluate the potential of high-resolution multi-temporal (growing season and end of the growing season) Worldview-3 imagery in mapping poplar plantations in the Akyaz? district of Sakarya, Turkey. For this purpose, pixel- and object-based image analysis with up-to-date ensemble learning algorithms, namely random forest (RF), categorical boosting (CB), and extreme gradient boosting tree (XGB), were employed for mapping poplar fields. Results indicated that the object-based classification approach provided statistically significant improvements in map-level (about 4%) and class-level accuracy (e.g., approximately 7% and %2 for poplar and young poplar classes, respectively) than pixel-based classification. While the CB performed superior classification performance for the object-based approach (92.56%), the highest classification performance was obtained with the XGB algorithm for the pixel-based approach (90.42%) for the end of the growing season data. McNemar’s statistical test also confirmed that the performances of CB and XGB algorithms were statistically similar in pixel-based classification. Finally, analysis of multi-season images revealed that sensitivity of the vegetation phenology and seasonal effects considerably affect the separability of poplar tree species.     

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming (SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction. Considering the strong nonlinearity of engine, a predictive model is established by Back Propagation (BP) neural network for the entire flight envelope, whose input and output are determined with random forest algorithm and actual situation analysis. Rolling optimization typically uses SQP as the optimization algorithm, but SQP algorithm is easy to trap into local optimization. Therefore, the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP. In addition to the traditional three parts of model predictive control, an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain. Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy, and the proposed control system can achieve good tracking performance with the limited parameters within the safe range.     

某电子设备耐大量级振动组合式减振器研制

某电子设备在飞行过程中遭受均方根加速度高达38 g的振动环境,为了降低其振动响应,采用有限元分析和试验验证相结合的方法,研制了一种新型的组合式橡胶减振器。通过与T型减振器进行对比试验,结果表明,设计的组合式橡胶减振器使电子设备的随机振动响应由38 g下降到11 g,减振效率高达71%,且共振峰放大倍数小于3。     

A robust Poisson multi-Bernoulli filter for multi-target tracking based on arithmetic average fusion

The coalescence and missed detection are two key challenges in Multi-Target Tracking (MTT). To balance the tracking accuracy and real-time performance, the existing Random Finite Set (RFS) based filters are generally difficult to handle the above problems simultaneously, such as the Track-Oriented marginal Multi-Bernoulli/Poisson (TOMB/P) and Measurement-Oriented marginal Multi-Bernoulli/Poisson (MOMB/P) filters. Based on the Arithmetic Average (AA) fusion rule, this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli (PMB) filter, which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence. In order to fuse the different PMB distributions, the Bernoulli components in different Multi-Bernoulli (MB) distributions are associated with each other by Kullback-Leibler Divergence (KLD) minimization. Moreover, an adaptive AA fusion rule is designed on the basis of the exponential fusion weights, which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT. Finally, by comparing with the TOMB/P and MOMB/P filters, the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.     

“珠海一号”高光谱数据城市不透水面提取研究

不透水面是评估城市化进程和评价城市环境的重要参考指标，研究城市不透水面的空间覆盖程度对城市生态环境保护、宜居城市建设具有重要意义。本文以“珠海一号”高光谱影像为数据源、珠海市陆域为研究区域，选取支持向量机和随机森林机器学习算法，结合光谱特征，实现城市不透水面信息提取。结果表明：使用“珠海一号”高光谱数据提取不透水面可行性较强，且支持向量机算法更适用于“珠海一号”不透水面提取，总体精度和Kappa系数分别达到92.4%、0.78；基于“珠海一号”高光谱数据进行城市不透水面提取，可为城市规划建设、分析城市土地扩张、评估城市灾害风险提供可靠理论依据和数据支撑。