target recognition

target recognition的基本解释

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目标识别

与target recognition相关的网络例句 [注：此内容来源于网络，仅供参考]

In this chapter, three kinds of target recognition methods are performed, which are:①Target recognition method based on the description of polarization parameter plane. The echo polarization states of target are projected onto the polarization state plane described by the ellipticity ε and the tilt angle τ of the polarization ellipse, the change of parameter following ferquency becomes the chart. According to the changing trait of the chart, the multidimensional polarization feature space of target has been contructed. Furthermore, a series of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.②Target recognition method based on the description of Poincare polarization sphere. The echo polarization states of target expressed by Stokes vector are projected onto the Poincare polarization sphere. The conception of polarization ferquency stability, which is used in describing the dynamic distribution characteristics of the target echo polarization states on Poincare polarization sphere, has been defined. A group of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.③Target recognition method based on the description of frequency sensitivity. In accordance with the conception of the polarization state distance defined on Poincare polarization sphere, the frequency sensibility of the physical structure property of target has been investigated, the frequency distribution feature curves in PSD domain are obtained, and targets'features are extracted by means of the curve-fitting method with Least Square Criterion.

这章具体研究了基于三种极化散射特性描述的相应的目标识别方法：①基于极化参数平面描述的目标识别方法，将目标回波极化状态投影到以极化椭圆参数，即椭圆率角ε和倾角τ表征的极化状态平面上，参数随观测频率的变化就形成了图，根据图的变化特点构造了目标的多维极化特征空间，并提取了不敏感目标姿态变化的极化特征参数组来设计目标的识别器结构；②基于Poincare极化球面描述的目标识别方法，采用Stokes矢量表征目标回波的极化状态，并将其投影到描述极化状态的Poincare极化球面上，定义了极化频率稳定度的概念用以刻画目标回波极化状态在Poincare极化球面上的动态分布信息，提取了准方位不变性的目标极化特征，最后设计了目标的识别器结构；③基于频率敏感性描述的目标识别方法，通过在Poincare极化球面上所定义的极化状态距离的概念，研究的是复杂目标物理结构特性对探测信号频率的敏感程度问题，获得了在极化状态距离下的频率分布特性曲线，采用最小二乘估计曲线拟合方法，它既用于极化特征的降维，同时又直接将拟合参数作为目标的分类特征。

A character-codes model based pattern recognition method by using amplitude data of targets frequency response is proposed after the establishment of high frequency response equation of radar targets. The method uses the same unvariant character-codes to express the same target of different angles and uses sequential separating of regions of target's reference variables to obtain multiple coding vectors of all regions for every target. The category and pose of target can be determined after a two-staged classification processing such as coding processing of target range profile data and errors correcting processing of coded data. The two-stage classification results are used to target identification as posterior hypothesis to be tested by sufficient convincing and necessary convincing. The character-codes model based pattern recognition method is also adaptive to variation of target angles and controllable in error probability. Time-consuming and complicated iterative computation is not necessary in the train process of pattern recognition.

首先建立了雷达目标的高频频率响应方程，这种方程将目标多散射中心理论与目标局部谐振理论有机地结合起来；进而提出了目标多频响应结构成像方法，这种方法将瑞利区频率响应或谐振区频率响应曲线的不同部位用各种不同的近似方法去逼近，通过特征谱估计方法将目标的局部频响特征与宽带距离响应特征结合起来；然后提出了一种基于姿态角变化区域的序贯划分与多模匹配的特征编码识别方法，这种方法具有能适应目标姿态角的变化以及能控制识别的差错概率α等优点，在目标识别时只需要目标在少数有限个频率激励下的幅频响应数据。

In chapter 5, in allusion to the shortcoming of the existing high resolution radar target detection algorithm, taking the problem of high resolution radar target detection as the problem of true-false target recognition, and borrowing ideas from the dealing with novelty problem, this paper introduces one-class SVM into high resolution radar true-false target recognition for the first time. That can provide a new idea for solving high resolution radar true-false target recognition problem.

第五章针对现有的高分辨雷达目标检测算法的缺陷，将高分辨雷达目标检测问题等效为真假目标识别问题，并借鉴处理异常值问题的思想，首次将1类支持向量机引入高分辨雷达真假目标识别之中，为解决高分辨雷达真假目标识别问题提供了一条崭新的思路。

与target recognition相关的网络解释 [注：此内容来源于网络，仅供参考]

fingerprint recognition：指纹辨识

生物辨识技术发展至今,利用生理特徵为大宗,有指纹辨识(Fingerprint Recognition)、人脸(Face Recognition)、虹膜(Iris Recognition)、静脉纹(Vein Recognition)等等,而利用人类行为则有声音、签名甚至步行轨迹等各种模式.

target recuperability：目标復元性

target recognition chart 目標認定圖 | target recuperability 目標復元性 | target return 目標回波

character recognition device：字符识别设备

character recognition 字符识别 | character recognition device 字符识别设备 | character recognition system 字符识别系统