时频域并行捕获算法

针对DS/FH(Direct Sequence/Frequency Hopping)混合扩频测控信号的快速捕获,经典的时域并行方法或频域并行方法已不能满足需求.为了进一步提高载波捕获精度,在PMF-FFT(Partial Match Filter-Fast Fourier Transform)算法基础上,提出了一种反馈式结构的时频域并行捕获算法,通过PMF-FFT算法粗估载波频偏,利用一种Quinn频率插值算法提高频偏估计精度,并使用估计结果调整本地载波,再次进行捕获.理论分析和仿真结果表明,该算法与PMF-FFT算法相比,提高了载波捕获精度,在大频偏低信噪比条件下增加了频率分析带宽、减少了平均捕获时间.     

扩频测控体制信号捕获方法分析

扩频综合基带设备集成了遥控、遥测、测距、测速等多种通用功能,在扩频测控体制的发展中占据重要的地位.针对综合基带设备的复杂需求,分析了传统信号捕获方法的局限性,在此基础上提出一种改进的扩频测控信号捕获方法.对载波多普勒范围进行子区间划分,采用二次频率调谐和分段积分累加来扩展频率捕获范围.引入非线性变换解决非相干测控体制下基带数据翻转造成的捕获性能恶化问题.通过仿真结果验证方法的有效性.最后分析了改进方法的捕获性能,可以为综合基带设备研制和测控体制参数设计提供参考.     

二维分形海面电磁后向散射系数的分析与计算

根据改进的二维分形海面模型推导了一种在不同极化方式下(HH水平极化,VV垂直极化)新的电磁后向散射系数计算方法,对多个海浪波长区域内所有分辨单元后向散射系数进行平均统计.在时域内研究了海水介电常数、电磁波入射角、入射频率以及风速风向对后向散射系数的影响,运用盒维数方法计算了该散射系数的分形维数.最后通过数值分析方法证明了该散射系数实部与虚部曲线是分形的,并且与二维分形海面的分形维数相等,从而表明该海面的雷达回波信号也具有分形特性,为运用分形理论研究海面微弱目标检测提供了一个重要方法.     

单频RTK动态精度检测法及实验验证

单频RTK技术在高精度测绘、无人驾驶等领域有着广泛应用，针对单频RTK的动态定位精度量化问题，提出了一种易于操作、适用地域广、不需要额外辅助设备的单频RTK动态精度检测法。首先，在地面建立基准直线; 然后，沿基准直线以走停模式测量RTK静态、动态组合数据，将静态数据利用整体最小二乘法拟合得到检核直线，并以此为参考评定动态定位精度; 最后，进行可靠性检验。精度评定时，以动态点到检核直线的平均偏离作为动态精度指标，采用间距误差作为检验所提方法可靠性的指标。实验结果表明，所提方法具有较高的可靠性，可以准确量化单频RTK约2~5 cm的动态定位精度。      

Modified fast Fourier transform in FBMC for satellite communications

In this paper,a Doppler scaling fast Fourier transform (Doppler-FFT) algorithm for filter bank multi-carrier (FBMC) is proposed,which can efficiently eliminate the impact of the Doppler scaling in satellite communications.By introducing a Doppler scaling factor into the butterfly structure of the fast Fourier transform (FFT) algorithm,the proposed algorithm eliminates the differences between the Doppler shifts of the received subcarriers,and maintains the same order of computational complexity compared to that of the traditional FFT.In the process of using the new method,the Doppler scaling should be estimated by calculating the orbital data in advance.Thus,the inter-symbol interference (ISI) and the inter-carrier interference (ICI) can be completely eliminated,and the signal to interference and noise ratio (SINR) will not be affected.Simulation results also show that the proposed algorithm can achieve a 0.4 dB performance gain compared to the frequency domain equalization (FDE) algorithm in satellite communications.     

A New Probabilistic Transformation in Generalized Power Space

The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being's subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.     

Development of a scheduling algorithm and GUI for autonomous satellite missions

In this paper, a scheduling optimization algorithm is developed and verified for autonomous satellite mission operations. As satellite control and operational techniques continue to develop, satellite missions become more complicated and the overall quantity of tasks within the missions also increases. These changes require more specific consideration and a huge amount of computational resources, for scheduling the satellite missions. In addition, there is a certain level of repetition in satellite mission scheduling activities, and hence it is highly recommended that the operation manager carefully considers and builds some appropriate strategy for performing the operations autonomously. A good strategy to adopt is to develop scheduling optimization algorithms, because it is difficult for humans to consider the many mission parameters and constraints simultaneously. In this paper, a new genetic algorithm is applied to simulations of an actual satellite mission scheduling problem, and an appropriate GUI design is considered for an autonomous satellite mission operation. It is expected that the scheduling optimization algorithm and the GUI can improve the overall efficiency in practical satellite mission operations.     

高分辨率星载斜视SAR的姿态导引

高分辨星载斜视合成孔径雷达回波信号中,多普勒中心频率随距离向的变化通常是十分显著的.这种变化导致系统需要更高的脉冲重复频率来实现方位向无混叠采样.然而,过高的脉冲重复频率将引起严重的距离模糊、极大的数据率,并将限制成像区域大小.本文推导了一种新的全零多普勒导引方法,并在此基础上提出了一种最小化多普勒中心频率随距离变化的姿态导引方法,从而能够最大限度地降低系统脉冲重复频率.根据星地空间几何关系和姿态导引原理,文中推导了实现全零多普勒导引的偏航角和俯仰角的解析表达式,并进一步给出了最小化多普勒中心频率随距离变化的姿态导引规律.最后通过计算机仿真,验证了该方法的有效性.     

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm(MPEA) framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is proposed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution(CC) algorithm combined with non-dominated sorting genetic algorithm II(NSGA-II) is introduced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multiobjective genetic algorithm(MOGA), multi-objective evolutionary algorithm based on decomposition(MOEA/D), CC-based multi-objective algorithm(CCMA) as well as other two MPEAs with different migration interval strategies.     

Improved Algorithm of Variable Bandwidth Kernel Particle Filter

Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm, a self-adiusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution, the bandwidth is dynamically adjusted, and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile, the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.