微型飞行器小展弦比薄翼流场中的翼尖涡特性分析

采用虚拟压缩方法求解三维不可压缩N-S方程,数值模拟了无弯度、有弯度两类微型飞行器低雷诺数小展弦比薄翼的流场,将得到的结果与实验进行了对比,数据间吻合较好.然后在此基础上分析了小展弦比薄翼的气动力特性,详细研究了小展弦比薄翼流场中翼尖涡的涡态结构及形成规律,在对小展弦比薄翼流场尾迹区的翼尖涡采用近似模型的基础上,对尾迹区翼尖涡的强度进行了分析.结果表明,翼尖涡是影响小展弦比薄翼气动力和流动分离特性的一个重要因素.     

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.     

滑翔式空中靶标总体设计与弹道仿真

空中靶标作为一种消耗性的无人飞行器,在防空反导武器系统试验训练中具有不可或缺的重要作用,须统筹考虑其逼真性和经济性。为此,提出了滑翔式空中靶标系统发展思路与工作原理,设计了滑翔式空靶总体方案,并利用Datcom和Matlab对其滑翔供靶弹道进行了仿真分析。仿真计算表明,弹道性能满足亚音速俯冲类目标模拟供靶要求,系统具有高性能、低成本、使用安全等显著特点。     

一种无人机自主避障与目标追踪方法

针对无人机自主避障与目标追踪问题，以深度Q网络(DQN)算法为基础，提出一种多经验池深度Q网络(MP DQN)算法，使无人机避障与追踪的成功率和算法的收敛性得到优化。更进一步，赋予无人机环境感知能力，并在奖励函数中设计了方向奖惩函数，提升了无人机对环境的泛化能力以及算法的整体性能。仿真结果表明，相较于DQN和双重DQN(DDQN)算法，MP DQN算法具有更快的收敛速度、更短的追踪路径和更强的环境适应性。     

面向轻体无人船舶的自供能毛细波推进器研究

毛细波推进器作为一种新型动力装置在无人船舶、医疗机器人等领域有着应用前景，然而其能量供给方式与推力特性之间的关系有待深入探索。本文提出一种基于摩擦纳米发电机的自供能推进方式，其可利用环境中的风能、波浪能等作为能量源，驱动介电润湿单元产生毛细波，并利用反推力推进船舶。在输出电压520 V，频率5 Hz的情况下，成功实现对一重量为3.42 g的泡沫船实现推进。本文探索了一种不依赖传统能源与结构的新型船舶推进模式，为今后轻型船舶的动力提供了新的解决思路。     

A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into sever...     

Trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

The interception problem of Hypersonic Gliding Vehicles (HGVs) has been an important aspect of missile defense systems. In order to provide interceptors with accurate information of target trajectory, a model based on an improved Long Short-Time Memory (LSTM) network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target. Firstly, for trajectory prediction required by intercepting guidance laws, the altitude, velocity and velocity direction of the target are formulated in the form of analytic functions, consisting of linear decay terms and amplitude decay sinusoidal terms. Then, the dynamic characteristics of the model parameters are analyzed, and the target trajectory prediction pipeline is proposed with the prediction error considered. Finally, an improved LSTM network is designed to estimate parameters in a dynamically-updated manner, and estimation results are used for the calculation of the final trajectory prediction pipeline. The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account. Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’ trajectory prediction which is conducive to increasing the interception success rate.     

某型无人机螺旋桨的设计与气动性能分析

为了提高某型无人机(UAV)的航时,螺旋桨应该具有高的巡航效率、足够的爬升拉力.利用高效率螺旋桨设计方法设计了某型无人机的两叶木质螺旋桨,获得了螺旋桨的弦长和桨叶角分布,基于片条理论计算了螺旋桨的气动性能,进行了螺旋桨的地面静态试验.结果表明:螺旋桨性能计算结果与台架试验结果具有较好的一致性,螺旋桨性能计算的巡航效率为...     

An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.     

落角与视场约束制导控制一体化策略

针对考虑视场(FOV)约束和落角约束的高超声速飞行器高精度打击问题，提出一种基于自适应动态规划(ADP)的新型制导控制一体化(IGC)策略。首先设计一种融合视场角与落角约束的视场角指令，在视场角精确跟踪的同时实现精确命中并满足两种约束，从而将约束问题转化为跟踪问题；然后，借助干扰观测技术估计制导控制一体化模型中不确定性并引入到性能指标设计中，又同时将视场角约束与落角约束考虑进去，设计基于自适应动态规划的制导控制一体化方法。利用ADP的强化学习思想求解出最优控制策略，既保证高超声速飞行器的精准打击，又满足视场角约束与落角约束，而且兼顾了对不确定性的鲁棒性。仿真结果验证了本文所提方法的有效性与优势。