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。     

点阵结构空气舵及内部流体热流固耦合研究

开展了钛合金TC4材料激光粉末床熔融(LPBF)工艺研究,在此基础上设计了多孔轻质空气舵模型,并基于有限差分法(FDM),采用三维流固耦合共轭传热数值计算方法,利用流体体积法(VOF)追踪流体自由液面,研究了典型的点阵夹层结构的空气舵内部冷却液动态换热过程的相互影响过程,考虑了冷却液与钛合金材料蒙皮间的耦合传热及湍流换热。结果表明,空气舵的内部流体压强随速度的增加而增加,从而导致流体出口的速度增加。当压强增加到一定程度时,流体的出口以水柱形式喷出。虽然较大的流体速度可以带走较多的热量,但是影响远小于对压强的影响。综合考虑空气舵的服役要求,获得了合适的冷却水入口速度。     

基于CDKF的快速协方差交叉融合跟踪算法研究

随着目标抗干扰能力的增强，单一寻的制导方式很难完成对目标的稳定跟踪和精确打击，需采用多种探测器作为传感器，提供多种观测数据以实现对目标的稳定跟踪和精确打击。建立了适当的目标运动模型和观测模型，利用中心差分卡尔曼滤波（CDKF）变换处理模型的非线性问题，避免了求解复杂的雅克比矩阵。对于分布式多传感器融合，传统的方法多采用协方差交叉（CI）融合方法，但是这类方法需要寻优求解。而快速协方差交叉（FCI）则不需要进行寻优过程，且计算量小。在此基础上，提出了用于多传感器目标跟踪的CDKF-FCI融合算法。最后，对算法进行了仿真分析，并进一步验证了提出算法的有效性。     

利用增量相位和星光仰角增强XNAV

针对X射线脉冲星导航系统(XNAV)中过程噪声统计特性难以准确获取，对其不当假设导致滤波器估计性能不佳的问题，提出基于自适应差分卡尔曼滤波器(ADDF)的多信息融合算法。为了降低导航误差，在传统脉冲星计时观测的基础上，增加恒星星光仰角及两个时刻间的相位增量观测量，共同增强XNAV。首先，分别建立计时观测模型、相位增量模型及星光仰角模型；然后将多信息测量模型集成到卫星轨道动力学方程中，以建立ADDF滤波模型；最后对所提方法进行仿真验证。实验结果表明，在相同的初始状态和初始噪声误差条件下，与传统X射线脉冲星导航算法相比，多信息融合算法能将导航位置估计精度提高70%以上，位置估计误差降低到 200 m 左右，速度估计精度提高40%以上，且ADDF性能优于无迹卡尔曼滤波器。     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

多机构比对融合的分布式InSAR编队星间基线确定

星间基线高精度确定是分布式干涉合成孔径雷达（InSAR）系统完成科学任务的重要保证,受星载全球定位系统（GPS）接收机连续跟踪弧段短、个别弧段共视GPS卫星个数少或模糊度固定成功率低、频繁轨道机动等因素影响,分布式InSAR高精度基线确定仍有不可靠的风险。通过多机构产品互比来识别基线精度较差的时间段,降低不可靠风险,并通过多机构产品融合进一步提高基线精度。选用重力反演与气候实验（GRACE）卫星数据进行实验,国防科技大学（NDT）和西安测绘研究所（CHS）采用不同的基线处理软件和简化动力学策略,保证了各自的基线产品具有一定的独立性。实验表明,多机构互比对可以有效识别基线精度较差的时间段,NDT和CHS的基线产品之间具有很好的一致性,互比对残差的均方根（RMS）在R、T、N方向分别为0.7、0.9、0.7 mm,二者之间没发现明显系统偏差,大约97.86%的基线三维互比对残差量级在2 mm以内。两个机构基线产品融合后发现可进一步降低基线产品中的随机波动误差,K/Ka波段测距（KBR）系统校核结果表明融合基线产品精度较NDT基线产品提高8.97%,较CHS基线产品提高29.21%。     

基于气路参数融合的涡扇发动机性能退化预测

针对单参数驱动的涡扇发动机性能退化预测精度不高的问题,提出了一种基于气路参数融合的涡扇发动机性能退化预测的方法。通过监测发动机性能退化过程中多源参数,采用专家经验和核主成分分析相结合的方法,进行发动机性能参数的选择和融合,从而构建健康参数。基于非线性Wiener过程构建涡扇发动机退化模型,采用极大似然方法求得发动机退化模型的离线参数估计值;由于不同发动机性能退化的差异性,基于贝叶斯更新理念对随机参数进行实时更新,可以实现对单台发动机的性能退化实时预测。通过实例验证,采用此方法在预测末端方均根误差为0.028 3,整体预测精度提升了54.5%,可以辅助指导维修决策。      

融合粗糙集与D-S证据理论的航空装备故障诊断

针对航空电子装备故障诊断中出现的多源诊断信息存在冲突的情况,基于粗糙集与证据理论在处理不确定问题时的优势,提出了一种融合粗糙集与证据理论的故障诊断方法.该方法利用粗糙集将信息源给出的诊断数据转化为证据理论中的mass函数,进行结果融合.同时,该方法给出边界粗糙熵的定义,并基于边界粗糙熵获得反映各信息源在诊断融合过程中重要度的动态权重参数,提出一种新的证据理论的冲突合成规则.仿真实验表明,该方法可以有效地提升诊断信息融合结果的准确性,在航空电子装备故障诊断方面有较好的实用价值.      

Real-time mosaicking for infrared videos from an oblique sweeping camera

Image mosaicking is widely used in Geographic Information Systems (GISs) for large-scale ground surface analysis. However, most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation. In this paper, we propose a novel and practical algorithm for real-time infrared video mosaicking. To achieve this, a novel fast template matching algorithm based on Sum of Cosine Differences (SCD) is proposed to coarsely match the sequential images. The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform (FFT). We also propose a novel fast Least Squares Matching (LSM) algorithm for inter-frame fine registration, which can significantly reduce the computation without degrading the matching accuracy. In addition, the proposed fast LSM can effectively adapt for noise degradation and geometric distortion. Based on the proposed fast template matching algorithm and fine registration algorithm, we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.