运动模糊车牌图像的恢复与定位

从车辆运动模糊图像产生的机理出发,分析运动模糊图像的退化模型,提出一种改进的参数估计方法进行运动模糊车牌图像的恢复与定位.首先,对运动模糊图像进行两次傅立叶变换后通过Radon变换以估计运动模糊方向,其次,利用微分自相关得到运动模糊长度,最后,结合边缘检测和数学形态学方法进行车牌的定位.实验结果表明,对于运动模糊的车牌图像,该方法能够有效地实现运动模糊图像的恢复与车牌定位.     

利用运动特性消除多目标时差定位模糊

时差定位系统由于方程多解、高重频等原因造成的定位模糊问题一直是关注的热点。从工程应用出发,提出了一种利用运动目标特性消除多目标时差定位模糊的方法。该方法在不增加设备成本的基础上在航迹起始的过程中排除虚假定位点,有效地消除了目标定位模糊。     

一种VSAR地面目标估计和定位新方法

针对速度合成孔径雷达(VSAR)地面运动目标方位定位,在距离-多普勒-速度域提出了一种基于改进Radon变换的目标径向速度估计新方法。基于VSAR地面运动目标信号模型,首先通过改进Radon变换实现模糊数和待定距离估计。进而,利用滑窗处理确定目标正确距离和模糊多普勒中心,实现运动目标速度估计和正确定位。新方法通过级联处理实现速度估计和定位,运算量小。最后,仿真结果证明了方法的正确性和有效性。     

基于模糊贴近的网络故障数据快速定位方法实现

传统的网络故障数据定位方法大都基于固定指标分析告警信息,实现故障信号的定位,一旦指标数量过高则容易出现故障信号定位误差过高的问题。提出一种基于模糊贴近算法的网络故障数据快速定位方法,该方法包括塑造针对网络业务的网络故障数据告警关联性分析模型、采集网络故障数据模糊告警贴近指标,以及实现基于贴近指标推理的网络故障数据定位3个模块。系统将当前的网络事实告警项进行模糊化处理转换成模糊告警,由模糊匹配在贴近指标库中发现与之匹配的全部贴近指标,并采集匹配度最高的贴近指标当成推理触发指标。从该指标中提取故障数据模糊告警关联信息,通过模糊合成策略,推理出网络故障数据的模糊告警结论。实验结果表明,所提方法对网络故障数据进行定位具有较高的定位精度,并且消耗能量较低。     

一种无参考运动模糊图像的模糊度估计方法

模糊度估计是模糊图像质量评价的重要研究内容。从研究基于图像边缘特征去评价模糊度出发,文章提出一种结合目标对称的轮廓特性估计运动模糊图像模糊度的方法。在无参考图像的情况下,根据边缘的成像特点从模糊图像中定位目标的关键边缘点。由于运动方向上目标的边缘包含着模糊信息,根据原本对称的目标在运动方向上和垂直于运动方向上所呈现出来的轮廓差异来测量图像的运动模糊尺度。实验结果表明,该算法能从正方形或圆形目标的单幅运动模糊图像中较准确地估计模糊度。     

基于混合积累的VSAR地面运动目标检测和定位

基于多通道复图像联合处理,速度合成孔径雷达（VSAR）可有效抑制地物杂波,实现地面运动目标检测和定位。但运动目标图像散焦和速度模糊也限制了VSAR的微弱目标检测性能,同时导致了快速目标的方位定位模糊问题。为此,提出了基于距离-多普勒-速度域混合积累的VSAR运动目标处理新方法,显著改善了微弱运动目标检测性能,并可实现快速目标的速度解模糊和正确定位。新方法无需改变VSAR的系统结构,工程实用价值高。最后,数值仿真实验的结果证明了新方法的有效性。     

时差定位中解高重频信号定位模糊研究

时差定位体制由于定位精度较高得到广泛应用,但长基线定位系统存在高重频信号的定位模糊问题。针对该问题,根据目标运动引起的径向偏移对到达时间产生微量调制这一规律,提出了径向微量偏移法,并讨论该方法的适用性,给出了算法相关处理流程。仿真结果表明,在目标具备一定径向速度分量时,该方法可以在较短时间内有效解定位模糊。     

基于模糊函数-雷顿变换的SAR运动目标成像

该文从一个新的角度赋予模糊函数以新的用途。近似为线性调频信号的SAR运动目标回波信号的模糊度函数在模糊平面内具有很高的能量集中性。联合雷顿变换可以求出线性调频信号的调频率。在此基础上本文提出了基于模糊函数-雷顿变换的SAR运动目标成像算法,计算机仿真结果验证了算法的可行性。     

基于毫米波InISAR成像的运动目标探测与定位

介绍了基于运动补偿的运动目标ISAR成像方法,将雷达成像的概念引入到低信噪比运动目标探测的过程中,在成像的同时获取目标的径向和横向运动速度,采用多基线图像干涉处理的方法完成运动目标的定位和横向尺寸估计.建立了运动目标成像几何模型,给出了多天线时分复用接收机的通道间信号补偿方法,以及多基线相位解缠实现不模糊干涉测角定位的方法,分析了信噪比对图像干涉定位精度的影响.利用仿真数据和实际数据验证了本方法的有效性.     

基于联合像素干涉统计特性的三通道动目标检测方法

本文提出了一种基于联合像素的三通道地面运动目标检测（GMTI）方法。首先分析联合像素处理后的两多视SAR图像于涉统计特性,建立幅相联合的概率统计模型,分析虚警和漏警,考虑到最小可检测速度内存在速度模糊,采用能量重心法解模糊,最后将干涉幅度和解模糊后的干涉相位联合检测动目标。实测数据处理结果验证了该方法的有效性。     

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications.In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution.Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices.In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed.The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions.Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms.With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.     

地面三维目标的探测识别系统研究

针对人员、车辆等地面目标运动时产生的地震动信号,利用地震动传感器采集这些信号并作预处理,再通过模糊识别算法对目标特征进行分类识别,进而构建了一个地面三维目标的探测识别系统.     

Energy-Efficient Ranging for Post-Facto Self-Localization in Mobile Underwater Networks

Many ocean processes, both biological and physical, greatly depend on and interact with the intrinsic current dynamics of the underwater environment. A promising approach to understand small and large scale spatio-temporal correlations of these processes is to deploy a networked swarm of drifters that float freely with ocean currents. They form a coordinated distributed sampling system that observes ocean processes within their own moving frame of reference. As data interpretation is impossible without knowledge of sampling positions, a method is required to localize the drifters. Furthermore, the localization has to be repeated periodically as the network topology changes due to the inherent motion of the vehicles. This paper proposes a novel energy-aware, distributed solution based on inter-drifter range measurements. It leverages the realization that actual position estimation can be performed after the mission is over. The proposed broadcast-based solution achieves sufficient localization accuracy with an extremely low overhead: around 0.5 transmissions per node per localization.     

装甲车辆与地面背景的热交互作用及红外仿真

为了全面揭示装甲车辆与地面背景之间热交互作用对地面背景温度以及红外辐射特性的影响,建立了装甲车辆和地面背景的温度和红外辐射特性的模型,重点考虑了装甲车辆与地面之间的传热。通过对地面和履带相关力学关系的引入,建立运动车辆和地面之间热学以及力学的模型,采用了动网格方法对车辆目标在运动后对地面所产生的沉陷现象及在地面上留下的热痕迹进行模拟仿真。基于红外辐射理论,综合考虑自身辐射、反射辐射以及大气传输特性,计算了车辆在3种不同状态下与地面背景之间的热交互作用及红外辐射特征分布,并比较了模拟温度与实验测量温度的误差,验证了模型的精度。仿真结果表明:该方法对地面目标的隐身设计和隐身技术评估具有十分重要的意义。     

一种智能物联网交通管理系统中静止车辆检测新方法

在智能物联网交通管理系统中,车辆检测是关键技术之一。以往检测车辆的方法都是基于运动差分的思想,只能检测出运动的车辆。文章提出一种检测静止的、即在视频序列中车辆位置保持不变车辆的方法,使用一个滑动窗扫描道路区域。实验表明,该方法具有较好的检测效果。     

Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference System

The main objective of this work is to automatically detect moving vehicles on the road. Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier is adopted in this paper to classify moving vehicles on road. An input traffic video scenes are taken with vertical and horizontal positioned cameras. The proposed system contains six major steps such as preprocessing, vehicle detection, tracking, structural matching, feature extraction and classification. In this proposed method, preprocessing consists of color conversion and noise removal. Vehicle detection is performed by using background subtraction and Otsu thresholding algorithm. Kalman filter is used in the third step to track moving vehicles in successive frames. In the fourth step, Active Shape Modelling method is used to recover the 3D shape of the vehicle in order to find the boundaries of vehicle. In the fifth step, features of the detected vehicles are extracted by Harrish corner detector, log Gabor filter and these features are taken into account to classify the types of vehicle. Finally, ANFIS is proposed to classify the vehicles which is trained by updating the membership function. Experimentation results provides better accuracy rate and low mean error rate when compared with the state of art methods.     

视频图像中的车辆检测及阴影去除方法研究

视频监控系统成为近年来的研究热点。文中针对交通智能视频检测系统的车辆检测问题,采用基于高斯混合模型方法来检测视频中的运动车辆,结合车辆阴影纹理不变的特性分割阴影,实现了运动车辆的检查识别和阴影去除。仿真实验达到了预期效果。     

基于双星TDOA和主星DOA的空中动目标联合定位技术

现有基于星载的空中动目标定位方法工程实现复杂度与代价过大,本文提出了一种应用于空中动目标定位的新型双星定位技术,联合双星到达时差信息（TDOA）和主星来波方向信息（DOA）可以获得目标的三维定位.通过矢量求解方法,建立了空中动目标定位的数学模型.同时,在考虑多种误差因素的前提下,建立了用于Monte Carlo分析的定位误差模型.最后,以空间站及其辅星作为应用场景进行了算例仿真.结果表明该技术能够实现空中动目标的地面航迹和三维位置的精确估计,可应用于空间站与卫星联合的空中动目标联合定位系统.     

基于图像处理的交通路口智能播报系统的研究

从智能交通系统的基本理论着手,介绍了将图像处理技术应用到智能交通系统的基本思路,并详细阐述了整个系统的算法设计。主要优势是对帧间差分法进行了改进优化,即改进的基于边缘信息的邻域帧间差分法,使得改进后的算法可以实时进行运动车辆检测,然后通过设置虚拟线来对车辆的参数进行间接的检测,最后通过LED提示行人和车辆。经实验验证改进后的算法简单方便且性能良好,能够准确的检测出运动车辆,增强了系统的可靠性。     

Seismic source model for moving vehicles

We develop a method for the loading of ground by moving vehicles in large finite-difference time-domain simulations of seismic wave propagation. The objective is to realistically produce two distinct types of ground loading for either wheeled or tracked vehicles in our propagation models: lower frequency loading associated with suspension dynamics and higher frequency impulsive loading associated with tire treads or wheels rolling over individual track blocks. These loading characteristics are important because field measurements show that vehicle ground forcing in both frequency bands produces seismic surface waves that networked sensors can remotely process for security applications. The method utilizes a vehicle-dynamics model to calculate a response to vehicle acceleration and ground features such as bumps; calculates forces transmitted to the ground; distributes these forces to staggered points of a finite-difference model; and simulates seismic wave propagation away from the vehicle. We demonstrate the method using bounce-and-pitch models of wheeled and tracked vehicles. We show that by carefully preprocessing force inputs, we can accurately simulate wave propagation and seismic signatures in finite-difference analyses of vehicles moving continuously over terrain.