A strategic flight conflict avoidance approach based on a memetic algorithm

Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.     

A practical nonlinear robust control approach of electro-hydraulic load simulator

This paper studies a nonlinear robust control algorithm of the electro-hydraulic load simulator （EHLS）. The tracking performance of the EHLS is mainly limited by the actuator＇s motion disturbance, flow nonlinearity, and friction, etc. The developed controller is developed based on the nonlinear motion loading model. The problems of the actuator＇s disturbance and flow nonlinearity are considered. To address the friction problem, the friction model of the loading motor is identified experimentally. The friction disturbance is compensated using the obtained friction model. Therefore, this paper considers the main three factors comprehensively. The developed algorithm is easy to apply since the controller can be obtained just with one step back-stepping design. The stability of the developed algorithm is proven via Lyapunov analysis. Both co-simulation and experiments are performed to verify the effectiveness of this method.     

An extended chirp scaling algorithm for spaceborne sliding spotlight synthetic aperture radar imaging

A system impulse response with low sidelobes is critical in synthetic aperture radar（SAR） images because sidelobes contribute to noise and interfere with nearby scatterers. However,the conventional tricks of sidelobe suppression are unable to be exactly applied to the case of spaceborne sliding spotlight SAR due to great azimuth shifts in both time and frequency domains. In this paper, an extended chirp scaling algorithm is presented for spaceborne sliding spotlight SAR data imaging. The proposed algorithm firstly uses the spectral analysis（SPECAN） technique to avoid the azimuth spectrum folding effect and then employs the chirp scaling（CS） algorithm to achieve data focusing, i.e., the so-called two-step approach. To suppress the sidelobe level, an efficient strategy for the azimuth spectral weighting which only involves matrix multiplications and short fast Fourier transformations（FFTs） is proposed, which is a post-process executed on the focused SAR image and particularly simple to be implemented. The SAR image processed by the proposed extended CS algorithm is very precise and perfectly phase-preserving. In the end, computer simulation results verify the analysis and confirm the validity of the proposed algorithm.     

基于无网格算法的化学非平衡流数值模拟

为了验证AUSMPW+格式在无网格算法的精度等以及无网格算法能否用于复杂化学非平衡流数值模拟中,首先将AUSMPW+格式推广到无网格算法中,给出了AUSMPW+在无网格算法中的具体形式。其次,将无网格算法应用于带化学反应的多组分气体Euler方程的求解,给出无网格条件下求解化学非平衡流的具体过程。最后,对NACA0012跨声速绕流场、高超声速弹丸绕流场、氢气/氧气诱导燃烧流场、球锥高超声速绕流场、楔体斜爆轰流场进行了数值模拟。数值模拟结果与相关文献计算结果和实验结果吻合较好,表明了AUSMPW+格式在无网格算法中表现较高精度。同时,采用无网格算法能较好地模拟复杂化学非平衡流场,正确分辨复杂的物理现象,为化学非平衡流数值模拟提供一种新的算法,拓宽了无网格算法的应用范围。     

转子-支承耦合结构的模型修正研究

在转子-支承耦合结构的动力学分析过程中,结构模态参数计算结果与实验测试结果往往存在较大差异。针对此问题,应用优化算法对结构动力学模型进行修正,使计算模型能更准确地反映结构特性。建立由梁单元、盘单元、弹性支承单元组成的结构动力学有限元模型;选择优化目标函数、设计参数,并对各参数进行灵敏度分析。应用粒子群算法对选取的设计参数进行优化,得到最优模型。利用转子-滚动轴承实验器进行验证,结果表明,方法能够有效地实现转子-支承耦合结构的动力学模型修正,修正后模型更接近实际结构。     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。     

单天基平台测向的空间非合作目标跟踪

针对仅含角度测量信息的单个天基平台可观测性较弱的问题,提出了一种含脉冲机动检测的空间非合作目标跟踪算法,并设计了非合作目标实时跟踪数据处理流程.该算法利用抗差估计技术和UKF（Unscented Kalman Filter,无迹卡尔曼滤波）算法构造目标跟踪滤波器,并综合残差多项式拟合和新息分布特征等方法实现目标机动检测,在天基平台观测信息类型有限和观测几何较差的情况下,可以同时排除孤立野值和成片测量野值的影响,实现非合作机动目标的连续稳定跟踪.数值实验验证了算法的可行性和有效性,也表明了跟踪精度和可靠性与测量精度密切相关.     

用于雷达方位超分辨的约束迭代Tikhonov正则化算法

针对Tikhonov正则化算法噪声适应能力差和不便于引进额外的约束对解进行限制等缺点,利用迭代Tik-honov正则化算法对雷达方位超分辨进行研究,证明了迭代Tikhonov正则化反卷积公式的收敛性,分析了迭代Tik-honov正则化算法的频域性质,在存在噪声的情况下,提出相应的噪声抑制方法,得到约束迭代Tikhonov正则化算法。针对不同信噪比情况进行了计算机仿真实验,结果表明,与迭代Tikhonov正则化算法相比,约束迭代Tikhonov正则化算法具有较强的噪声适应能力,与常用的约束迭代方法（CID）算法相比,具有较快的收敛速度,初步验证了算法的有效性。     

激光陀螺四位置寻北仪提高精度方法

为消除激光陀螺零偏和常值漂移的影响,采用四位置方案来实现高精度寻北。本文简述了单轴激光陀螺四位置寻北方案,给出了陀螺敏感轴与机体系前向偏差角标定方法,研究了寻北仪在倾斜基座下数据补偿算法。本文研究了由于在四个位置上电机转动不到位所引起的误差补偿算法。通过对寻北仪系统测试,采用电机转动不到位补偿后数据标准差降低了0.02º,激光陀螺寻北仪系统达到了高精度快速寻北的要求。