用投影预变换提高自适应波束形成的稳健性

在快拍数较少和（或）存在系统误差时，自适应波束形成的性能会变差，特别是会引起方向图副瓣电平升高——波束畸变。本文提出的投影变换法利用干扰方向的初略估计，在未精确预知阵列流形的条件下，可以显著改善自适应方向图的副瓣性能，并提高自适应算法的收敛性，计算机仿真结果验证了这一优点。     

斜视圆迹环扫SAR模式特性分析及成像方法简

Circular scanning SAR(CSSAR) is a new mode for fast large area SAR imaging by increasing the azimuth scanning speed. The squint CSSAR possesses the advantages that the side-looking CSSAR does not have due to its unique observing direction. However, the available signal characteristic analysis and imaging method for side-looking CSSAR become questionable when applied to the squint CSSAR. This paper establishes the echo range model and analyzes several parameters such as azimuth scanning speed, Doppler characteristics and azimuth resolution according to the moving feature of squint CSSAR. Furthermore, the internal relationship between the squint CSSAR and the classical stripmap mode is revealed. The range approximation model as well as the corresponding imaging method is analyzed. Computer simulation results validate the conclusions and the imaging method.     

稳健二进神经网络的几何训练

在二进神经网络的规划划分训练方法基础上,针对稳键二进神经网络稳健神经元的特点,讨论了稳健分类超平面的几何构造方法,并提出了相应的训练算法,包括隐层神经元的几何训练和输出神经无的进化训练。实验表明,该算法对复杂Boole函数的稳健二进神经网络的实现是有效且可行的。     

应用于洋流探测的改进MUSIC方法

对传统的MUSIC方法进行修正估计洋流方位，即在应用MUSIC方法之前，将阵列的协方差矩阵Toeplitz化，理论分析和仿真表明这样可有效地减小信号之闻相关性的影响，从而大大提高谱估计的质量．通过对实测数据的处理表明修正的MUSIC方法可较好地进行洋流方位探测．     

时—空二维时域平滑MUSIC算法的统计特性分析

在阵列输出数据长度一定的情况下，讨论了阵列输出数据时域平滑预处理时－空二维ＭＵＳＩＣ算法的数学机理及其统计性能分析，得出了最佳时域平滑长度。探讨了在信号发生时域或空域兼并时，时－空二维ＭＵＳＩＣ算法的有效性。计算机模拟与理论分析结果一致。     

Group-normalized processing of complex wavelet packets

Linear phase is not possible for real valued FIR QMF, while linear phase FIR biorthogonal wavelet filter banks make the mean squared error of the constructed signal exceed that of the quantization error. W Lawton' s method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets that are symmetrical and unitary orthogonal; then well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. Since the traditional wavelel packets transform coefficients do not exactly represent the strength of signal components, a modified adaptive wavelets transform, group-normalized wavelet packet transform (GNWPT), is presented and utilized for target extraction from formidable clutter or noises with the time-frequency masking technique. The extended definition of lp-norm entropy improves the performance cf GNWPT. Similar method can also be applied to image enhancement, clutter and noise suppression, optimal detection     

基于复数高分辨距离像特征提取的雷达自动目标识别

高分辨距离像(HRRP)已成为雷达自动目标识别(RATR)领域研究的热点。由于目标复数HRRP的初相具有不确定性(本文称其为初相敏感性),在RATR中通常使用的HRRP为实向量(文中称为实HRRP),即目标复基频回波信号的幅度像,而完全丢弃了相位信息。事实上,复HRRP中除初相外的其他相位信息也应该是对识别有利的。文中提出了一种复HRRP的特征提取方法,该方法得到的复特征向量与初相无关但却保留了距离单元间的差分相位信息,称为相位差分复特征向量。若基于散射点模型理论分析,这种复特征向量的物理意义与实HRRP类似(只是包含了差分相位信息),因此,对实HRRP适用的识别、建模和预处理方法同样适用于该复特征向量。同时,相位差分复特征向量的各分量具有类Gauss性,更有利于统计识别。基于实测数据的识别实验表明,只要在特征提取时适当选取距离单元间隔参数,文中提出的相位差分复特征向量可以取得比实HRRP更好的识别效果。     

Grading learning for blind source separation

By generalizing the learning rate parameter to a learning rate matrix, this paper proposes a grading learning algorithm for blind source separation. The whole learning process is divided into three stages: initial stage, capturing stage and tracking stage. In different stages, different learning rates are used for each output component, which is determined by its dependency on other output components. It is shown that the grading learning algorithm is equivariant and can keep the separating matrix from becoming singular. Simulations show that the proposed algorithm can achieve faster convergence, better steady-state performance and higher numerical robustness, as compared with the existing algorithms using fixed, time-descending and adaptive learning rates.     

基于距离Keystone变换的改进的多普勒调频率估计方法

多普勒调频率估计是SAR成像处理中的一个重要步骤。针对聚束、滑动聚束SAR采用全孔径算法进行成像时的调频率估计问题,该文提出一种改进的平移相关(SAC)算法。将距离Keystone变换引入到SAC算法中,去除了互相关峰值位置与目标距离间的耦合关系,从而消除了聚焦深度的限制。由于没有时域补零操作,调频率估计过程中将会出现模糊,针对此,给出了一种基于最小熵准则的模糊数估计方法。仿真及实测数据处理结果验证了所提方法的有效性。     

基于TSB-HMM模型的雷达高分辨距离像目标识别方法

针对雷达高分辨距离像(HRRP)的识别问题,该文提出了一种基于时域特征的截断Stick-Breaking过程隐马尔可夫模型(TSB-HMM),并建立了基于TSB-HMM模型的分层识别算法,利用TSB-HMM模型结合时域特征和功率谱特征对HRRP进行分层识别。实测数据的实验结果表明,该方法是一种有效的雷达HRRP识别方法,分层识别的算法可极大提高目标的平均识别率。特别是在训练样本数极少的情况下,TSB-HMM模型仍能获得较好的识别性能。