雷达波形优化的特征互信息方法

针对扩展目标识别中距离敏感性的波形优化问题,在通用注水法的基础上提出了基于特征提取的波形优化设计方法,简称为特征互信息方法．该方法通过最大化识别特征与目标特性之间的互信息以达到增加各类目标可分性的目的．仿真结果表明,相对于线性调频信号和注水法优化信号,特征互信息方法优化信号可以得到更大的互信息,并能较好地提高目标的识别性能．     

基于互信息理论的MIMO天波超视距雷达波形优化方法

多输入多输出（MIMO）天波超视距雷达（OTHR）技术在雷达发射端发射低增益宽波束,在接收端进行波束形成得到高增益的窄波束,可以满足多层电离层探测和杂波抑制的需求。该文将两层电离层结构应用到MIMO-OTHR中,并针对OTHR杂噪比（CNR）比较高的特点,提出一种互信息理论方法对发射波形进行自适应优化。仿真结果表明该方法明显提升了目标距离分辨率和检测概率,说明在MIMO-OTHR中,经过合理的波形优化,可以利用多径回波改善雷达系统性能。     

基于互信息的立体匹配方法

介绍一种基于灰度互信息的立体匹配方法,用于从双目图中获得视差图。该方法将灰度互信息作为衡量输入图像对相似度的指标,先构建单像素点的代价,再在代价聚合环节加入邻域点的约束,使用多方向的扫描线优化来优化代价聚合函数。反复地进行优化过程,让输出的视差图渐渐地接近于真实情况,使用随机图作为迭代初始状态,迭代固定次数后输出。灰度互信息作为代价标准,使得该方法在代价的计算环节比一般的全局匹配方法更快,并对光照具有一定鲁棒性,领域点的约束让视差图更加平滑稠密。     

一种基于遗传算法的DDS脉冲波形优化方法

直接数字频率合成（DDs——Digital Direct Frequency Synthesis）是一种新的频率合成技术，具有频率分辨率高、频率切换快、超宽的相对带宽、连续的相位特性和全数字化等优点，DDS实际上是波形的合成技术，可以输出高稳定度的任意波形．对脉冲产生型DDS中输出信号的过冲和振铃进行了讨论，提出了用遗传算法优化脉冲波形以减少过冲和振铃的方法，并用计算机仿真结果进行证明。     

一种基于互信息与梯度的图像精确配准方法

提出一种利用基于梯度加权的灰度归一化互信息相似性测度,并采用凹函数递减的权衡比重的粒子群优化算法作为搜索策略的多模图像精确配准方法。传统的基于灰度互信息配准方法往往只考虑灰度相关性,忽略或不当引入图像空间特征信息,配准容易陷入局部极值,从而出现误配。将灰度与梯度特征有效融合,即梯度加权到灰度互信息中,同时考虑了2幅图像的灰度统计相关性和图像空间特征信息,提高了多模图像配准的精度与稳定性。通过对遥感图像的拟配准与MR-PET医学图像的实际配准,证明了该方法效果良好,算法稳定,配准的准确率和参数精度都得到明显的提高。     

稳健的MIMO雷达发射波形和接收滤波器联合优化设计

针对杂波条件下目标反射信号方向不确定的情况,提出一种稳健的MIMO雷达发射波形和接收滤波器联合优化设计方法.在考虑各阵元发射功率相等的约束条件下,利用发射加权矩阵构造不确定集范围内关于输出信干噪比(SINR)的Maxmin优化模型;在此基础上,利用序列优化、半正定松弛和Charnes-Cooper转换,将非凸的联合优化问题转化为关于发射波形和接收滤波器的凸优化问题进行迭代求解;最后通过随机向量合成方法计算最终发射波形和接收滤波器,并对算法计算复杂度和收敛性给出分析和证明.所提方法在满足阵元发射功率一致的约束条件下,提高了算法稳健性并降低了计算复杂度.仿真实验证明了算法的有效性.     

一种基于联合特征矢量的雷达目标识别方法

综合波形特征和小波分解能量特征作为联合特征矢量,利用概率神经网络对目标类型和数量进行一体化识别,从对真实雷达视频数据进行的实验证实了这点。     

雷达视频回波的一种采集方法

本介绍了一种雷达回波采集卡，该采集卡的特点是，针对指定的目标的回波进行采集，存储的冗余数据少，可作为进行目标自动识别的数据采集装置。     

一种改进的基于互信息的三维医学图像配准算法

基于互信息的三维医学图像配准是配准的一大热点．本文提出的配准方法主要是对互信息测度的公式进行简化，修正了Powell算法的方向替换方法，保持了原搜索方向，提高了配准速度．合理的设置收敛阈值，并用多分辨率策略进行了图像配准测试．实验表明，改进后的互信息测度可以达到亚像素精度，而且在配准速度上有了明显提高．     

一种利用AD9850设计的雷达波形产生器

利用ＡＤ９８５０设计了一种新的雷达波形产生器，分析了它的性能。     

Adaptive waveform design for target detection in the wideband radar system

The target detection performance of radar can be improved by transmitting a waveform which matches the high-resolution range profile (HRRP) of the target. However, a serious degradation of the autocorrelation property always occurs in the matched waveform, which has a bad effect on radar parameter estimation. To solve this problem, a generalized model of HRRP which consists of the deterministic term and random term is introduced under the consideration of variations in the target impulse response. Based on the study of the generalized likelihood ratio test (GLRT) detector and the objective functions for waveform design, an iterative algorithm with the criteria of maximizing the signal to noise ratio (SNR) and minimizing the minimum mean square error (MMSE) is proposed. Simulation results indicate that the design method can make a tradeoff between the performances of estimation and detection, and sequentially improve the detection performance based on the on-line estimates of the HRRP.     

MIMO radar waveform design in the presence of clutter and colored noise

The existing MIMO radar waveform design methods only deal with the optimization of radar waveform with the presence of clutter or colored noise, and unfortunately can not get the optimal waveform with the scenario when the clutter and colored noise coexist, which is more realistic in practical applications. To address this problem, a novel MIMO radar waveform design method based on mutual information in the presence of clutter and colored noise is proposed. The proposed scheme considers the influence of the target's response, clutter and colored noise on the optimization of the radar waveform. The mathematical expression for the transmit waveform matrix is achieved by theoretical derivation. What is more important, an optimal pairing of the eigenvectors of the target, clutter and colored noise is given and the power allocation for each antenna is optimized by the water-filling method. Simulation results show that when the total transmit power is 10dB, the proposed method's MI is 8bit and 4bit more than that of the general water-filling method and the method which only considers the pairing of the target's response and noise, respectively.     

基于矩阵加权多模型融合的认知跟踪波形设计

针对机动目标状态跟踪问题,认知雷达能够调整发射端波形来获取持续、稳健目标跟踪信息.本文基于矩阵加权多模型融合思想引入一种新的面向机动目标跟踪的认知雷达自适应波形设计方法(Adaptive waveform design method based on Matrix-weighted Interacting Multiple Model,AMIMM).首先,利用多模型思路对机动目标状态进行建模,并考虑各模型目标状态估计及其误差协方差矩阵中元素间相关性,以矩阵加权融合方式代替传统概率加权方式,进而构造基于矩阵加权多模型信息融合的跟踪算法框架;然后,以多模型状态融合后的状态估计误差协方差矩阵为基准,利用特征值分解(Eigen Value Decomposition,EVD)技术求取融合后状态估计误差协方差矩阵对应椭圆参数;最后,通过分数阶傅里叶变换(fractional Fourier transform,FrFT)来旋转雷达量测误差椭圆,使得量测误差椭圆与融合后目标状态估计误差椭圆正交,从而获得下一时刻认知波形参数,实现波形自适应捷变.仿真实验表明,与当前流行多种算法相比,本文所提算法能够进一步提高机动目标跟踪精度和稳健性.     

基于互信息的特征子集选择

通过互信息的思想提出一个新的评价函数来评价属性之间的相关性,并结合LV算法进行特征子集选择.结果表明,该方法对分类问题效果明显.     

采用OFDM-LFM的MIMO雷达高速目标波形设计

单基地多输入多输出雷达由于其宽发射波束的特性而能有效解决目标高速运动跨越发射波束带来的能量损失问题;但是现有波形对速度容忍性较差,高速目标回波匹配滤波能量损失较大．因此,有必要研究适用于高速目标背景下的多输入多输出雷达波形． 文中提出了一种基于正交频分复用线性调频信号的多输入多输出波形． 该波形不同发射通道分时复用一组不同频段的线性调频信号,从而得到带宽相同的可分离多路多输入多输出雷达波形． 仿真结果表明,文中设计的多输入多输出雷达波形能有效降低高速目标回波匹配滤波的能量损失,适用于高速目标背景．     

Optimal radar waveform design method in colored and non-Gaussian noise

This paper considers the design of optimal waveforms for detection of point targets in colored non-Gaussian noise. We present the likelihood ratio processor and derive an expression for its output signal-to-noise ratio (SNR), and then use this as the basis for a cost function in a waveform design optimization which is solved by using a one dimensional alternating maximization algorithm. Finally simulations are performed to verify the effectiveness of the new waveforms.     

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information (MI),a feature selection algorithm were proposed based on improved mutual information.The improved mutual information algorithm,which is on the basis of traditional improved mutual information methods that enhance the MI value of negative characteristics and feature’s frequency,supports the concept of concentration degree and dispersion degree.In accordance with the concept of concentration degree and dispersion degree,formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these.In this paper,the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods.The experimental results showed that the improved mutual information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain.Through the introduction of the concept of concentration degree and dispersion degree,the improved mutual information feature selection method greatly improves the performance of text classification system.     

Joint adaptive waveform and baseline range design for bistatic radar

The problems of joint adaptive waveform design and baseline range design for bistatic radar to maximize the practical radar resolution were considered. Distinguishing from the conventional ambiguity function （AF）-based resolution which is only related with the transmitted waveform and bistatic geometry and could be regarded as the potential resolution of a bistatic radar system, the practical resolution involves the effect of waveform, signal-to-noise ratio （SNR） as well as the measurement model. Thus, it is more practical and will have further significant application in target detection and tracking. The constraint optimization procedure of joint adaptive waveform design and baseline range design for maximizing the practical resolution of bistatic radar system under dynamic target scenario was devised. Simulation results show that the range and velocity resolution are enhanced according to the adaptive waveform and bistatic radar configuration.     

基于小波分解和最大互信息的图像配准方法研究

研究了基于最大互信息图像配准的理论和实现算法,提出了将小波分解与之相结合的算法,证明了该算法优于基于最大互信息的图像配准。通过实验数据比较可以得出,提出的基于小波分解和最大互信息的图像配准的算法可以有效地提高配准的速度和具有较好的配准效果。