一种实现图像感兴趣区域嵌入式编码的改进算法

对图像感兴趣区域ROI(Region of Interest)的支持是新一代静止图像压缩标准JPEG2000的一项重要功能。文章利用嵌入式编码理论,针对零树小波编码EZW(Embed-ded Zerotree Wavelet Coding)算法的效率不足提出了一种改进方法。通过基于对EZW方法的适当改进并用于ROI编码,实现了图像感兴趣区域的高品质重建,进一步降低了编、解码复杂度和存储的空间,并提高了编码的效率。     

一种平稳小波方向能量阈值滤波的SAR图像去噪方法

由于SAR图像相干斑噪声是非高斯分布的,无法直接采用光学成像系统的去噪技术处理,因此,目前还没有真正的理想算法广泛适用于SAR图像去噪.在分析目前流行的空频域去噪方法优缺点的基础上,提出了1种小波域SAR图像去噪方法.为了克服离散小波变换缺乏平移不变性及方向选择性受限的缺点,该方法利用平稳小波将图像分解为低频逼近信号和...     

星载角跟踪接收机角误差包络检测

针对星载角跟踪接收机中角误差合成信号的特点，提出一种基于复Morlet组合小波的角误差信息提取方法。该方法首先对星载角跟踪接收机中角误差合成信号的频域特性进行分析，角误差包含在角误差合成信号的包络中，频率上则表现在调制基波的谐波分量处。然后采用Morlet小波作为母小波，根据角误差合成信号的频域特性选取合适的小波尺度参数和位置参数可以有效的提取合成信号包络信息，从而解算角误差信息。在调制基波频率确定的情况下，合适的小波尺度参数和位置参数能准确对调制基波谐波分量进行提取，从而实现角误差的包络检测。该方法对±10kHz范围内的多普勒频偏不敏感，解决载波多普勒估计不准情况下的角误差提取。最后通过计算机中的Matlab对Morlet组合小波角误差合成信号包络提取进行仿真，证明该方法的可行性和有效性。     

卫星线阵立体影像的倾斜仿射变换核线模型

核线约束是构建摄影测量立体模型的重要条件。由于卫星线阵影像的多中心投影特性，难以建立其严格的核线模型。文章从上下视差最小的同名核线特征出发，在仿射变换模型基础上引入核线倾角变化参数，提出一种新的倾斜仿射变换核线模型。通过“无误差”的虚拟同名点仿真试验表明，文章提出的模型受影像范围及地表高差影响较小，在影像区块6 000像元×6 000像元、地表高差1 000m情况下，对于不同形式卫星立体影像的模型误差均小于1个像元。进一步使用实际匹配同名点平差，实验表明：对于SPOT-5、“资源三号”和“高分七号”等卫星影像，文章模型误差相对于匹配观测误差可忽略，实际核线精度在整景影像范围内优于0.8像元，所生成核线影像能够满足后续密集匹配要求。新模型的精确性和普适性相比现有同类模型显著提升，可实际应用于卫星摄影测量处理。     

基于小波神经网络的PIM功率时间序列预测

无源互调（passive intermodulation，PIM）是影响通信系统性能的关键因素之一。研究表明，PIM信号并非一个定值，而是随时间变化的非平稳信号。基于PIM功率信号的时间序列特性，文章提出基于小波神经网络的PIM功率时间序列预测方法。首先，详细介绍小波神经网络预测模型及其预测方法；其次，以同轴连接器为验证对象，通过PIM实验测试系统获得3阶PIM功率的时间序列；最后，依据获得的PIM功率时间序列，结合构建的小波神经网络预测模型对后续的时间序列进行预测分析，并将预测结果与实验结果进行比较，从而验证小波神经网络在预测PIM功率时间序列方面的有效性。该研究对于开展PIM抑制技术具有一定的参考价值。     

基于遗传算法优化小波网络的柔性喷管力矩特性辨识方法

为准确辨识负载力矩并提高主动加载负载模拟的真实度，使用了一种基于遗传算法优化的神经网络辨识方法。使用小波分析方法对测试信号进行预处理，将消噪与分解后得到的信息作为神经网络训练的扩充样本，提高了辨识精度。使用遗传算法选择最优输入信息、网络结构和隐含层规模，加快网络收敛速度并简化计算过程，实现对柔性喷管力矩的快速准确辨识。仿真结果表明该辨识方法可以准确地描述柔性喷管在典型测试信号激励下的力矩特性，平均辨识误差为2%，对于实现精确主动加载控制和验证伺服控制性能具有重要意义。     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

润滑油红外光谱去噪预处理方法

采用小波包变换软阈值去噪方法对润滑油的红外光谱进行去噪预处理,结合偏最小二乘法进行定量预测,并与常用的平滑去噪方法进行对比。结果表明,小波包变换能有效地去除红外光谱的噪声,以此为基础建立的润滑油酸值模型的预测精度高于常用的平均平滑法和 Savitzky-Golay卷积平滑法这 2种平滑去噪方法的预测精度。     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.     

火箭飞行异常遥测天线实时跟踪角度预测研究

针对火箭飞行异常下的遥测天线继续实时跟踪难的问题,提出一种飞行异常后的实时跟踪角度的预测方法.首先介绍几种常用坐标系以及坐标系之间的转换关系,并将火箭飞行异常后的运动简化为考虑惯性速度的自由落体运动,给出自由落体速度模型和实时跟踪角度的计算方法步骤.设计仿真实验,验证方法的有效性,可以作为火箭飞行异常后遥测天线实时跟踪...