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.     

低成本卫星测控系统性能改进方法研究

首先针对TLE更新不及时、轨道递推准确性低的问题,提出低成本卫星测控系统(LCTTC)采用卫星GPS实测数据修正站心坐标系下目标轨道方法,提高跟踪精度;其次利用接收机的RSSI功能,给出了远程获取无人值守LCTTC灵敏度、评估其性能的方法,以及获取信噪比、发射功率等状态信息的方法,对卫星实时任务过程进行监测和异常诊断,减少了故障排查时间。     

极区横向地理系空间稳定型惯导算法研究

针对地心惯性系和当地地理系的空间稳定型惯导系统导航算法在极区导航失效的问题,提出了适用于空间稳定型惯导系统的极区导航算法。该算法通过伪经纬网构建了横坐标系参考框架,建立了横向地理系空间稳定型惯导系统力学编排,并在此基础上重新推导了通用的误差模型。最后,通过极点附近区域与穿越极点区域仿真分析了算法的极区有效性。仿真结果表明该算法在极点附近区域解算的伪航向角误差小于3′,伪经度误差小于4′;在穿越极点区域解算的伪纵摇角、伪横摇角误差小于0.3′,伪航向角误差小于3′,伪东向、伪北向速度误差小于1m/s,伪经度、伪纬度误差小于4.2′。该算法克服了极区导航计算溢出、误差放大等问题,提高了系统的极区导航精度,能够满足极区导航要求。     

Energy-efficient data collection for UAV-assisted IoT: Joint trajectory and resource optimization

Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. As energy-efficient operations are critical to prolong the lifetime of the energy-constrained IoT devices, the Unmanned Aerial Vehicle (UAV) can be dispatched to geographically approach the IoT clusters towards energy-efficient IoT transmissions. This paper intends to maximize the system energy efficiency by considering both the IoT transmission energy and UAV propulsion energy, where the UAV trajectory and IoT communication resources are jointly optimized. By applying large-system analysis and Dinkelbach method, the original fractional optimization is approximated and reformulated in the form of subtraction, and further a block coordinate descent framework is employed to update the UAV trajectory and IoT communication resources iteratively. Extensive simulation results are provided to corroborate the effectiveness of the proposed method.     

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.     

未知纬度条件下基于空间圆拟合的SINS初始对准方法

传统的捷联惯导系统(SINS)晃动基座初始对准算法,如积分双矢量方法和多矢量Wahba方法等均需要精确的纬度信息,粗对准偏航角误差较大.针对此特点,提出了一种未知纬度条件下基于空间圆拟合的SINS初始对准方法.根据重力矢量在惯性系中绕地轴旋转包含北向信息的特征,以初始时刻凝固载体坐标系作为惯性系,首先对重力矢量在凝固载体系内进行投影并滤波,对其矢量端点进行空间圆拟合,然后通过三角几何关系得到导航坐标系,从而完成对准过程.通过仿真验证,证实了该对准方法不需要已知对准点的精确位置信息,相比于积分双矢量方法与多矢量Wahba方法,偏航角对准精度分别提高了12.37'与5.10'.     

“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。