无人平台复杂地形探测的视触融合方法

为提高无人平台在复杂环境中的地形探测能力以及解决在小样本数据下识别地形困难的问题，提出了一种无人平台复杂地形探测的视触融合方法。在原始宽度学习的基础上，建立了多模态级联特征节点宽度学习框架。首先进行触觉和视觉初步特征提取和融合特征提取，随后将融合特征矩阵经宽度学习分类器得到地形识别的结果。最后，在自建的视觉-触觉地形 (V-T2)数据集进行了实验验证。结果表明，相比于传统的融合算法，提出的融合算法有很好的准确性和鲁棒性，为无人平台地形探测提供了有效的策略。     

基于DOA聚类算法的ARM抗有源诱偏技术研究

有源诱偏技术是雷达对抗反辐射导弹的一种重要手段,可以大大降低反辐射导弹的作战效能。为提高对敌目标的精确打击能力,在分析闪烁诱饵诱偏原理的基础上,针对有源诱偏干扰下被动雷达测角精度与稳定度不高的问题,通过对有源诱偏信号的时域特征进行分析,提出一种基于脉冲前沿检测的DOA聚类分选算法,找出前沿超前的辐射源信号,实现了高性能抗有源诱偏干扰和高精度抗干扰测向。仿真结果表明,该技术可以有效对抗四点源有源诱偏干扰。     

Statistical characteristics of low latitude Pc4s: Influence of solar wind and IMF parameters

Pc4 signatures for the year 2013, extracted from geomagnetic north–south and east–west components of induction coil magnetometer (LEMI 30) from low latitude station Desalpar (DSP), operated by Institute of Seismological Research (ISR), India have been investigated vis-à-vis the prevalent interplanetary parameters (IMF) as well as the geomagnetic activity indices. A clear dominance of Pc4-5 (467 events) over Pc3 (17 events) is observed. Local time variation of Pc4 shows a peak in the noon sector in both X and Y components. Our investigations show that the dominant peak frequency is 10 mHz at low latitude region. Correlations with solar wind and IMF parameters illustrate highest occurrence of Pc4 for a solar wind speed of 300–400 km/s and average IMF B field of 3–6 nT. The amplitude of Pc4s at DSP shows an increase with increasing solar wind speed, plasma density, solar wind dynamic pressure and average B field which is also reflected in the trend of frequency variation of these pulsations. We report that IMF clock angle at low latitude does not have influence on Pc4 occurrence. Based on the characteristics of these events, detected in latitudinally distributed stations from low and mid-latitudes from northern and southern hemisphere, we infer that modes were compressional, which could be driven by K-H instability or solar wind dynamic pressure, as compressional modes can propagate to low latitude with little attenuation.     

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

基于微型涡喷发动机热喷流的无源流体推力矢量喷管的控制规律

流体推力矢量喷管型面固定、活动部件少、结构重量轻，能够为高机动飞行器提供有效的飞行控制手段，但无源流体推力矢量喷管热喷流的偏转控制规律尚未完全掌握。为了推进无源流体推力矢量技术的实用化，本文设计研制了适用于微型涡喷发动机的耐高温喷管模型，对该喷管在微型涡喷发动机热喷流状态下的控制规律进行研究。利用非接触光学显示和测量手段——红外热成像拍摄和粒子图像测速（PIV）技术对主射流流动特性进行研究，获得流动矢量角随二次流控制阀门闭合度变化的控制规律；利用六分量盒式天平测力实验研究无源流体推力矢量喷管的力学特性，获得推力矢量角随二次流控制阀门闭合度变化的控制规律。研究结果表明：该构型喷管在微型涡喷发动机热喷流下主射流连续可控偏转，最大流动矢量角为-12.3°/12.3°，最大推力矢量角为-12.9°/12.8°，控制规律接近线性，不存在主射流偏转突跳问题。     

Influence of thickness and initial groove angle in M–K model on limit strain of 7B04 by considering through-thickness stress

Marciniack–Kuczinski (M–K) model is widely used to predict material’s forming limit curve (FLC). The prediction of FLC traditionally neglected through-thickness normal stress. However, it cannot be neglected in some forming processes. Much work has been done to study the effect of through-thickness normal stress on FLC with constant through-thickness normal stress or constant ratio of through-thickness normal stress and maximum principal stress. In addition, based on Nakazima test process, the ratio of through-thickness normal stress and maximum principal stress has been derived, which was a function of instantaneous thickness and loading path. Here, initial groove angle in M–K model was not considered. In this paper, uniaxial tension tests and Nakazima tests were performed on 7B04 aluminum alloy. Based on Hill 48 yield criterion and M–K model, the prediction model of FLC was established. The increase of thickness can enhance FLC. Meanwhile, it is necessary to consider through-thickness normal stress and initial groove angle in prediction model. On the left side of FLC, the effect of initial groove angle on FLC is weakened by increasing sheet thickness. On the right side of FLC, the effect of initial groove angle on FLC is strengthened by increasing sheet thickness. On the right side of FLC, the relation between limit strain points with different thicknesses is linear under one certain loading path. Thickness has decisive effect on through-thickness normal stress level and the changing trendy of through-thickness normal stress during calculation is different under different stress condition.     

先进战斗机过失速机动大气数据融合估计方法

可控的过失速机动是先进战斗机超机动性能的重要标志，飞机飞行包线的扩大已超出传统的大气数据系统测量范围，可靠的迎角、侧滑角、总压、静压等飞行大气数据是制约先进战斗机过失速机动中飞行控制的关键因素。以中国推力矢量验证机为对象，基于过失速机动飞行试验的数据，开展大气参数估计与验证研究。结合过失速机动的时间与空间特性，研究了基于风速、地速、空速矢量和惯性姿态、导航参数的大气参数融合计算方法；针对过失速大迎角状态下飞机周围气流非定常、模型非线性导致的融合大气参数误差的复杂特性，进一步构建深度神经网络，对机动状态融合迎角、侧滑角的强非线性误差进行拟合。仿真和飞行试验表明：该方法可在大迎角飞行状态下实现主要大气参数的融合估计，过失速机动过程中融合迎角误差优于2.3°，融合得到的大气参数可为过失速大迎角机动飞行控制提供可靠的大气参数状态反馈。     

移动机器人铣削制孔系统基准检测

结合航空大部件的数字化对接装配需求,设计了一套用于对接面铣削制孔的移动机器人加工系统,并提出具体的加工工艺流程。研究了基于激光轮廓扫描仪的大量散乱点云数据的预处理算法,提出基于栅格法和迭代拟合法的对接面特征提取算法。针对移动机器人加工系统在大场景下的高精度定位问题,提出基于扫描线法和最小二乘法原理的对接基准孔坐标找正算法。通过产品的加工实验验证,对接面铣削和制孔精度满足系统要求的各项技术指标,证明了本文提出的移动机器人铣削制孔系统的装配对接面加工方法能够精确地完成大部件数字化装配任务,对提高装配质量和效率具有重要意义。     

基于YOLO智能网络的红外弱小多目标检测技术

复杂背景下的红外弱小多目标检测是红外目标检测的难点,现有算法的稳健性难以满足实际弹载应用需求。针对弹载环境下的红外场景图像开展了基于典型深度学习网络模型的目标检测应用研究,提出一种基于YOLO(you only look once)网络的智能目标检测方法,通过高维特征学习表征和推理实现红外弱小多目标检测。采用传统的模板匹配算法和YOLO深度学习算法进行识别性能对比分析,验证了YOLO网络在红外弱小多目标检测方面的良好性能。实验结果表明:YOLO算法的检测概率可达92.2%,平均检测精度为0.844,与传统的模板匹配方法相比,YOLO具有明显的优势。     

某机载探测系统自主回收技术的实现

针对某机载探测系统探头回收自动化程度低的问题，提出了在无需操作员干预情况下实现探头自主回收的技术方法。采用了先进的控制算法，将缆位角与探头、缆长进行结合并引入了绞车控制系统，使绞车对探头的回收速度及缆长可进行实时调整，并给出了3种不同缆位角摆动条件下缆长与探头回收速度的控制曲线关系。研发了自主回收装置，设计了能够实现自主回收功能的探头回收机构。实验验证表明：在无需人为干预的条件下，在缆位控制系统及自主回收机构的配合下，系统实现了对探头快速、安全的回收。