航空发动机叶片外物损伤研究现状

 全面地介绍了航空发动机叶片外物损伤研究的现状。叶片鸟撞击损伤是一种软物损伤。从鸟撞击叶片过程的理论研究、计算研究和试验研究三个方面进行了详细地介绍。叶片冰撞击损伤既可能是软物损伤又可能是硬物损伤。叶片冰损伤的研究文献较少，对此进行了简要地介绍。叶片硬物损伤包括金属块、石块和冰块三种形式。对叶片硬物损伤研究的各个方面，包括叶片硬物损伤的形成、叶片硬物损伤后的疲劳强度、叶片由于硬物损伤而产生微裂纹和微裂纹增长等，进行了详细地介绍。最后，提出了国内进行叶片外物损伤的研究方向。     

飞船返回舱红外跟踪

提出一种联合大底检测的改进的Mean-Shift（均值移位）算法,将其应用于返回舱红外跟踪。新算法采用非均匀量化加权直方图构造目标特征矢量,以提高目标描述的准确性。为避免跟踪到大底上去,使用＂滑窗式＂大底检测算法实时修正Mean-Shift跟踪坐标。通过对飞船返回实况判读验证,提出的新算法处理速度达到20帧/s以上,并且能够可靠和准确地跟踪返回舱。     

基于随机有限集的空间碎片群运动状态估计

传统的空间目标监测是建立在单目标状态估计基础之上,在面对突发产生的大量空间碎片时,由于碎片尺寸小,且密集分布以"群"的方式出现,传统单目标处理方法很难奏效。以"群"整体作为处理对象,基于随机有限集(RFS)技术,对"群"的状态特征进行估计。为了解决漏检目标密度分配问题和轨迹关联问题,提出一种面向量测的改进集势概率假设密度(CPHD)滤波器,并结合滤波后的信息处理过程,完成了对低轨空间碎片群的目标密度分布、群内目标数以及群内显著目标的状态估计。在仿真实验中,提出的滤波器表现明显优于传统滤波器和标准CPHD滤波器,且在某些传统滤波器和标准CPHD滤波器已失效的情况下,所提技术仍能有效工作。     

Position and force control of coordinated multiple arms

A technique is presented for controlling multiple manipulators which are holding a single object and therefore form a closed kinematic chain. The object, which may or may not be in contact with a rigid environment, is assumed to be held rigidly by n robot end-effectors. The derivation is based on setting up constraint equations which reduce the 6×n degrees of freedom of n manipulators each having six joints. Additional constraint equations are considered when one or more degrees of freedom of the object is reduced due to external constraints. Utilizing the operational space dynamic equations, a decoupling controller is designed to control both the position and the interaction forces of the object with the environment. Simulation results for the control of a pair of two-link manipulators are presented     

基于FDTD与C＋＋的金属目标RCS省内存计算

利用C＋＋语言的面向对象特性,实现了金属目标雷达散射截面（Radar Cross Section,RCS）的省内存计算。一般用时域有限差分（Finite—Difference Time-Domain,FDTD）法计算金属目标的RCS时,没有考虑到金属目标内部电磁场量为零的特性,对金属目标内部也分配了大量的内存。将金属目标分为内外两部分,对内部区域分配内存消耗少的数组,利用C＋＋语言的多态性,将各个不同的区域连接成一个整体来计算,从而减少了内存消耗。数值计算结果表明,与普通计算方法相比,对计算精度没有影响,同时可以大幅节省内存消耗。     

Range-Doppler Imaging of Rotating Objects

During the integration time required to obtain fine Dopplerfrequency resolution in a range-Doppler imaging radar, a point on a rotating object may move through several range and Doppler resolution cells and produce a smeared image. This motion can be compensated by storing the appropriately processed return pulse, and the angular coordinates are determined by the angular coordinates of the radar antenna. The resulting stored data represents the three-dimensional Fourier transform of the object reflectivity density, and hence can be processed by an inverse Fourier transformation. Also included is an analysis of the three-dimensional radar/object geometry with separate source and receiver locations. The effects of various system aberrations are investigated and experimental results from a microwave test range which demonstrate the image improvement are presented.     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.     

基于小波变换和不变矩的车标识别方法

形状特征是目标识别的重要参数,小波变换的低频部分代表物体的总体形状特征,而图像中的噪声主要分布于高频部分。根据这一特征,利用小波变换消除噪声提取目标形状,进而利用特征不变矩距离进行分类,实现目标识别,将该方法应用在实测车标图像的识别中,结果表明识别效果较好。     

一种新型的智能非线性PID控制器

在分析总结标准PID控制器、各种变形PID控制器、非线性PID控制器特点的基础上，提出了一种智能非线性PID(INPID)型变结构控制器。将该INPID控制器应用于典型带延迟一阶对象和二阶对象控制，并与传统PID进行了对比，证明该控制器具有良好的控制效果、广泛的对象适应性和很强的鲁棒性。仿真结果表明．在控制量受限越强和系统参数变化范围越大的情况下，其优势越突出。     

Distributed sensor data fusion with binary decision trees

A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented     

基于深度学习的无人机航拍目标检测研究综述

目标检测是提高无人机（UAV）感知能力的关键技术之一,其研究对于无人机的应用有着重要意义。与基于手工特征的传统方法相比,基于卷积神经网络的深度学习方法具有强大的特征学习和表达能力,成为目前目标检测任务的主流算法。近年来,目标检测技术已经在自然场景图像上取得了一系列突破性进展,在无人机领域的研究也逐渐成为热点。首先系统阐述了基于深度学习的目标检测算法的研究进展,并总结了相关算法的优缺点。对常见的航空影像数据集进行了梳理并介绍了迁移学习的方法;从无人机影像背景复杂、目标较小、视场大、目标具有旋转性的特点出发,对无人机目标检测在近期的研究进行了归纳和分析。最后讨论了存在的问题和未来可能的发展方向。     

Bistatic Synthetic-Aperture Radar Imaging of Rotating Objects

The imaging properties of a focused radar aperture synthesized by object rotation are considered. The ring-shaped aperture synthesized by the object rotation can provide high-resolution two-dimensional imaging using only monochromatic irradiation. The case of monochromatic irradiation is extended to the polychromatic case and to equivalent bistatic geometries. These techniques offer practical methods for radar imaging of rotating objects with scattering centers visible over large angular extents.     

提取流管实验装置中声衬声阻抗的模态方法

发展了一种模态匹配方法进行有均匀流条件下敷设声衬的二维矩形流管中声传播的计算, 计算结果与NASA流管实验结果和CAA计算结果符合得很好.在此基础上, 着重介绍采用求极值的变尺度法优化目标函数和给定求解范围寻找目标函数最小值两种方法进行管内未知声阻抗的提取, 给出了算例验证, 并分析了随机误差和已知声压点数对计算结果的影响.      

面向对象方法的程序设计简述

首先回顾了面向对象的发展过程及其相关基本概念,介绍了面向对象的几种开发语言,重点描述了面向对象程序设计的优点和面向对象程序设计对软件开发过程的影响,与结构化程序设计比较,面向对象程序设计更易于实现对现实世界的描述,因而得到了迅速发展,对整个软件开发过程产生了深刻影响。     

弹道并行计算的设计与实现

多条弹道并行计算是目前作战仿真，多目标探测等领域所急需解决的问题，利用面向对象的软件设计方法，将弹道计算过程抽象化为多个对象，分别对各种对象进行封装，有效地解决了并行弹道计算中内存混乱，可靠性差等问题，实现了多条弹道并行计算。     

SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network, which can achieve good performance in terms of general object detectors. However, in the field of remote sensing image object detection, as pretrained models are significantly different from remote sensing data, it is meaningful to explore a train-from-scratch technique for remote sensing images. This paper proposes an object detection framework trained from scratch, SRS-Net, and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution module. Then, two necessary improvement principles are proposed: studying the role of normalization in the network structure, and improving data augmentation methods for remote sensing images. To evaluate the proposed framework, we performed many ablation experiments on the DIOR, DOTA, and AS datasets. The results show that whether using the improved backbone network, the normalization method or training data enhancement strategy, the performance of the object detection network trained from scratch increased. These principles compensate for the lack of pretrained models. Furthermore, we found that SRS-Net could achieve similar to or slightly better performance than baseline methods, and surpassed most advanced general detectors.     

HYBRID POSITION/FORCE MANIPULATION OF MULTIFINGERED HAND

ＨＹＢＲＩＤＰＯＳＩＴＩＯＮ／ＦＯＲＣＥＭＡＮＩＰＵＬＡＴＩＯＮＯＦＭＵＬＴＩＦＩＮＧＥＲＥＤＨＡＮＤＧＵＡＮＹｉｓｈｅｎｇ（管贻生）,ＺＨＡＮＧＱｉｘｉａｎ（张启先）（ＲｏｂｏｔｉｃｓＲｅｓｅａｒｃｈＩｎｓｔｉｔｕｔｅ,ＢｅｉｊｉｎｇＵｎｉｖｅｒｓ...     

Hermite插值结合FDTD法快速计算三维目标宽角度RCS

 Hermite插值结合时域有限差分(FDTD)法快速计算了三维目标的宽角度雷达散射截面(RCS)。一般用FDTD法计算散射体的宽角度RCS时,每改变一个入射角度,就需要重新用FDTD法计算一次。引入Hermite插值逼近方法可以节省计算时间。在整个入射角度范围内选定若干个入射角,对不同的入射角,分别用FDTD法计算得到外推面上各点的切向电磁场值及其对入射角的导数值,进而得到这些场值随入射角度变化的Hermite插值函数,然后用插值函数计算出全入射角度范围内外推面上各点的切向电磁场值,最后通过近远场变换得到宽角度RCS。计算结果表明在只有少数几个插值节点的情况下本文方法就能很好地逼近FDTD法的精确计算结果,节省了计算时间。     

运动平台上基于粒子滤波的目标跟踪技术

在固定平台上实现视频运动目标的准确跟踪本身就是一项富有挑战性的工作。何况在运动平台上,受到背景杂波、亮度变化以及摄像机运动等干扰的影响,要想成功地对目标进行跟踪则更加困难。提出将强度梯度跟踪器与粒子滤波相结合,实现对运动平台上的视频序列中的运动目标的准确跟踪。实验结果表明,跟踪方法能准确、可靠地自动跟踪视频序列中的运动目标。     

脉冲雷达探测中高轨道目标方法及试验

通过分析中高轨目标雷达回波信号特性,给出一种基于空间目标动力学约束的回波信号相参积累方法,介绍了信号积累模式下中高轨道目标的参数测量方法。通过对国内某型号雷达进行中高轨目标探测支路改造及相关试验,获取试验数据并对其进行分析,验证了原理的正确性和方法的可行性。