SINS／GPS组合系统的蒙特卡洛仿真

以考虑位置误差相关项的伪距率观测模型，对遥感中使用的SINS/GPS为距、伪距率组合系统进行了蒙特卡洛仿真。结果表明，组合系统的长期位置精度能达到5m以内；GPS数据更新率低于SINS，在GPS测量时间间隔内，组合系统的性能仅由SINS决定，虽然SINS的误差随时间积累，但在GPS测量时间间隔为秒数量级的情况下，即使采用中等精度的惯性的误差随时间积累，但在GPS测量时间间隔为秒数量级的情况下，即使采用中等精度的惯性仪表，其相对位置精度可达到厘米级（这里相对位置精度指组合系统在GPS测量时间间隔内位置误差的变化范围）。     

地-空导弹自适应最优制导律初探

本文从自适应控制的基本思想出发,利用最优控制理论,在建立地-空导弹最优制导律的基础上,考虑导弹质心的动力学方程,建立了扩维的系统状态方程和测量方程。再利用最优控制理论和非线性系统扩维Kalman滤波理论,推导出地-空导弹自适应最优制导律,进行数字计算,得到了比较满意的结果,为进一步探讨地-空导弹的自适应最优制导律提供了有价值的参考。     

自适应DBF与空间谱估计

讨论了自适应数字波束形成(DBF)技术和超分辨空间谱估计技术,并提出了一种基于信号协方差矩阵特征分解的时-空二维信号高分辨谱估计算法。以上技术应用于相控阵雷达系统,可以精确获得多个运动目标的方位信息和多普勒信息。文章最后给出了计算机模拟结果。     

应用加权中值滤波法检测薄壁梁式挠性器件下摆量

应用加权中值滤波法对薄壁梁式挠性器件下摆量进行高准确度CCD检测。薄壁梁式挠性器件在测量过程中测量图像存在高频抖动现象,因此测量准确度不高,运用加权中值滤波后使用最小二乘法细分技术,可以在实现提高测量准确度的同时,消除高频抖动带来的误差,从而可以有效提高CCD测量薄壁梁式挠性器件下摆量的准确度。实验表明,该方法比传统测量方法准确度提高20倍以上。     

无刷直流电动机的新型自适应模糊神经控制

 为无刷直流电动机提出了一种自适应模糊神经控制方法.这是一种建立在开关控制、模糊控制和自适应控制相结合基础上的控制方法,并用神经网络实现了模糊控制器和自适应机构.在无刷直流电动机的双闭环调速系统中,电流控制器是PI控制器;转速控制器是由1个开关控制器和1个包括自适应机构在内的模糊控制器相结合组成的,且用1个3层前向神经网络离线学习实现了模糊控制器,学习算法采用的是改进的BP算法.用1个单神经元通过在系统运行过程中的动态学习实现了自适应机构,学习算法选用了有监督的Hebb学习算法.由电机所处的运行阶段决定哪一个控制器工作.此控制算法的仿真结果说明,它使系统具有良好的动、静态特性和自适应性.     

平台摇摆对卡尔曼滤波跟踪精度的影响

通过建立目标相对运动坐标系和目标相对运动观测模型,研究了在平台摇摆影响下,跟踪系统观测到的目标运动状态的变化。在分析捷联垂直基准补偿原理的基础上建立了捷联垂直基准平台摇摆角补偿模型,建立的模型结合捷联垂直基准系统的测量能力对其补偿算法进行了理论推导,使模型适用于实际捷联垂直基准系统。通过建立模型以及仿真研究了平台摇摆作用下卡尔曼滤波跟踪精度的变化,指出了摆造成卡尔曼滤波跟踪精度降低甚至离散的主要原因在于模型误差增大。设计仿真实验验证了结论的正确性,为进一步改进跟踪手段提供了理论参考。     

A cost driven predictive maintenance policy for structural airframe maintenance

Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring (SHM) systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,lead ing to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter (EKF) with a first order perturbation (FOP) method.By using the information given by this prognostics method,a novel cost driven predictive maintenance (CDPM) policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is imple mented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies:scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.     

新一代战斗机非定常流动数值研究综述

新一代战斗机强调超机动能力和强隐身性，其中大攻角下的静态失速、动态失速及内埋弹仓绕流是与高机动和强隐身密切相关的、极具挑战的几类典型的非定常流动，它们对数值方法提出了极高的要求。为了高精度地仿真流场、清楚地揭示流动机理，有效地控制非定常流动，非常有必要发展高精度且高效率的RANS-LES混合方法体系，包含RANS-LES混合方法本身、与RANS-LES混合方法匹配的高精度自适应耗散格式、基准湍流模式、高质量计算网格、高精度时间推进方法、非定常量的统计方法等，具有极强的紧迫性。提出、发展、验证并应用该类方法数值仿真新一代战斗机（包括单独部件、组合体、甚至全机）的非定常流动，数值预测结果与风洞实验数据吻合良好；此类方法可为新型战斗机设计提供理论依据和分析手段。     

Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles

This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for realtime applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker's performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-ofthe-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker's robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016.     

自适应遥测信道速率的分包遥测调度方案

在卫星分包遥测静态分配适应单一遥测信道的方案基础上,提出了一种改进型动态自适应遥测信道速率的遥测分包方法,将包传输的控制参数由绝对参数优化为相对参数,从而满足不同信道速率、不同包源速率下的包传输适应性.该方法通过将遥测包周期计数与遥测包静态分配的传输周期的比值作为遥测包传输等待迫切比例系数,用相对的传输迫切比例系数值对全部遥测包进行排序,从大到小组入遥测信道下传.通过这种遥测包调度策略解决了遥测信道速率变换对包传输控制的影响.此外通过调节遥测包传输比例系数也能很好地解决遥测包数据率变化对信道传输的适应能力的影响,如数据率增加时整体降低包传输频率、数据率减少时整体加快包传输频率.