天气雷达网资料拼图方法研究

天气雷达组网拼图,是克服单部雷达探测范围有限,发挥多部雷达相互辅助大范围监测灾害性天气的有效手段。本文在分析现有天气雷达组网拼图方法和存在问题的基础上,对天气雷达组网拼图的资料网格化、重叠区域处理、资料投影等方面作了进一步研究。对雷达反射率因子网格化问题,对比分析了Barnes和双线性插值算法的特性,说明Barnes插值算法网格化资料平滑且能较好地保存雷达资料特征;在重叠区域的资料订正方面,采用概率分布方法,实现了对雷达资料作用距离和天线指北等系统误差的订正,结果表明订正后的资料在重叠区域中各雷达资料的吻合度得到了加强;最后采用兰勃特投影法将天气雷达组网拼图投影到统一底图中。     

相控阵测量雷达跟踪起伏动目标精度分析

目标不同的航路及其运动特点对雷达测量数据质量影响很大.为了估算整个航路目标动态定位精度,依雷达测量目标工作原理,给出航路分段处理原则.对雷达测量数据与GPS测量真值比对的一次差,提出了基于三次B样条的雷达系统误差分离方法.通过处理某飞行任务的大量测量数据,估算出了不同航路的雷达动态测距、测角系统误差和随机误差.结果表明,雷达测距系统误差与斜距成正比;测角系统误差为设计指标的2倍多;其随机误差与设计指标相符.     

多雷达多目标航迹匹配策略

在进行多雷达多目标航迹匹配时,由于测元系统误差随跟踪距离传播,雷达测量精度下降,易出现航迹匹配错误的情况。针对该情况,应用多测元非线性融合模型,采用样条约束的误差模型最佳弹道估计(Error Model Best Estimation of Trajectory,EMBET)方法对雷达测元系统误差进行自校准,将利用欧几里得距离进行航迹匹配的传统方法改为利用精度较高的雷达测距作差比较,有效解决了多雷达航迹匹配时的门限阈值合理设置的难题。仿真结果证明算法有效适用,可极大地提高多雷达多目标航迹匹配时的准确度,对完成多目标空域分布、目标识别等突防效果分析评估具有重要价值。     

多雷达组网信息融合系统空间配准方法研究

系统误差估计是空间配准中的关键环节,在多雷达组网系统中有着十分重要的意义.分析了系统误差估计的一般模型,从非线性估计问题的角度给出了系统误差估计的一般方法,使用最小二乘配准方法对某试验中的实测数据进行了验证,结果证明了方法的正确性和有效性,可为多雷达组网空间配准中系统误差估计问题提供一定的参考.     

雷达动态精度试验误差统计分析方法改进

动态精度飞行试验是雷达定型、鉴定和抽样试验的重点内容之一,通过对动态精度飞行试验中的误差统计分析方法进行分析,针对试验有效航次较少这一特点,将Bootstrap算法引入到雷达分组系统误差统计分析过程中,提出了一种改进的雷达动态精度试验误差统计分析方法,该方法能够充分利用有限航次数据信息,生成自助样本来模拟原始数据分布特性,提高误差估计精度,为减少试验航次、节约试验成本奠定了理论基础。     

基于无人机GPS的测量雷达标校方法研究

针对测量雷达常规标校方法存在的场地及设施限制性问题,提出了一种基于无人机加装GPS设备的雷达标校方法.利用无人机加装差分GPS设备作为跟踪目标并提供真值数据,根据系统误差修正模型,通过最小二乘法对雷达测量误差进行回归分析,解算出系统误差修正参数,即可完成雷达标校.通过模拟数据解算得出了该标校方法适用的航线范围,通过飞行测试对标校方法进行了验证,结果表明,利用解算得出的标定参数进行系统误差修正能够满足雷达精度要求,证明了该方法有效、可行.     

分布式2-D雷达网的高度估计技术

针对2D雷达组网高度估计问题,本文提出了一种基于局部雷达估计的目标高度估计方法。2D雷达由于不能获得目标的高度信息,其所得到的局部估计将会存在一定的系统误差。文中的推导结果表明,可以通过两部雷达的局部估计建立关于目标高度所产生系统误差的非线性模型。在此基础上,合适的滤波方法可以实现对目标高度的实时估计。仿真结果表明,该方法的收敛速度较快,可以有效估计目标的真实高度。     

基于可观测度评价的雷达系统误差估计

系统误差估计是雷达组网融合跟踪系统的重要环节,而可观测度又是决定误差估计效果的关键因素.通常情况下,可观测度难以定量描述,直接衡量系统的估计性能也就不易实现.提出了一种间接的方法评价系统的可观测度,并根据评价的结果判定估计的精度.对于可观测度较低的情况,改进了基于EKF的系统误差估计算法,提高了估计的精度.仿真实验表明,方法不仅能够有效评价系统的可观测度,并且在低可观测度条件下,改进算法的误差估计精度明显优于原有算法.     

单脉冲雷达测量误差修正方法研究

针对单脉冲雷达上存在的测量误差进行了详细的研究和分析,通过与单脉冲测量雷达数据的误差模型进行比较,并给出了比较的图形,判断出系统产生误差的原因,通过采取四象限比较法总结出了系统误差出现的规律,并由此推导出系统误差模型,从而实现了对系统误差进行有效的修正;同时运用自适应卡尔曼滤波的方法抑制了随机噪声,从而提高了雷达测量数据的处理精度,这一方法在实际应用中证明是行之有效的,并且取得了良好的效果。     

基于AIS的雷达高精度误差校准方法

利用AIS进行雷达标校在军事领域已得到广泛应用。但在标校过程中发现,校后雷达精度很多时候没有得到显著提高。从误差序列处理层面上对影响校后雷达精度的原因进行分析,提出一种基于AIS的雷达高精度误差校准方法。新方法首次提出在传统的校准流程中加入误差分布分析这一环节,并对关联、对准后的误差序列进行两次异常值筛选。通过实测数据证明,所提出的方法能够得到较准确的系统误差估计值,对雷达的系统误差进行了有效的校准。     

基于扩展Kalman滤波的空基多平台多传感器数据配准和目标跟踪算法

本文研究基于扩展Kalman滤波和多个空中移动平台的多传感器数据配准与目标跟踪问题.文中首先给出了空中移动平台传感器数据配准几何坐标转换算法;接着将目标运动模型和传感器配准误差模型组合在同一个状态方程中,然后利用扩展Kalman滤波方程进行估计.Monte-Carlo仿真表明,该方法能同时有效地估计目标运动状态和传感器配准误差.     

Multi-sensor calibration through iterative registration and fusion

In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods.     

Closed form solution for the sensor registration problem using only position information

Sensors, mounted on the dexterous end of a robot, can be used for feedback control or calibration. When you mount a sensor on a robot it becomes necessary to find the pose (orientation and position) of the sensor relative to the robot. This is the sensor registration problem. Many researchers have provided closed-form solutions to the sensor registration problem; however, the published solutions apply only to sensors that can measure a complete pose (three positions and three orientations). Many sensors, however, can provide only position information; they cannot measure the orientation of an object. This article provides a closed-form solution to the sensor registration problem applicable when: (1) the sensor can provide only position information and (2) the robot can move along and rotate about straight lines. © 1994 John Wiley & Sons, Inc.     

Distributed joint sensor registration and target tracking via sensor network

This paper deals with distributed registration of a sensor network for target tracking in the presence of false and/or missed measurements. Each sensor acquires measurements of the target position in local coordinates, having no knowledge about the relative positions (referred to as drift parameters) of its neighboring nodes. A distributed Bernoulli filter is run over the network to compute in each node a local posterior target density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback–Leibler divergence, is minimized with respect to the drift parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registration and target tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.     

A Theory of Minimal 3D Point to 3D Plane Registration and Its Generalization

Registration of 3D data is a key problem in many applications in computer vision, computer graphics and robotics. This paper provides a family of minimal solutions for the 3D-to-3D registration problem in which the 3D data are represented as points and planes. Such scenarios occur frequently when a 3D sensor provides 3D points and our goal is to register them to a 3D object represented by a set of planes. In order to compute the 6 degrees-of-freedom transformation between the sensor and the object, we need at least six points on three or more planes. We systematically investigate and develop pose estimation algorithms for several configurations, including all minimal configurations, that arise from the distribution of points on planes. We also identify the degenerate configurations in such registrations. The underlying algebraic equations used in many registration problems are the same and we show that many 2D-to-3D and 3D-to-3D pose estimation/registration algorithms involving points, lines, and planes can be mapped to the proposed framework. We validate our theory in simulations as well as in three real-world applications: registration of a robotic arm with an object using a contact sensor, registration of planar city models with 3D point clouds obtained using multi-view reconstruction, and registration between depth maps generated by a Kinect sensor.     

An iterative integrated framework for thermal–visible image registration,sensor fusion,and people tracking for video surveillance applications

In this work, we propose a new integrated framework that addresses the problems of thermal–visible video registration, sensor fusion, and people tracking for far-range videos. The video registration is based on a RANSAC trajectory-to-trajectory matching, which estimates an affine transformation matrix that maximizes the overlapping of thermal and visible foreground pixels. Sensor fusion uses the aligned images to compute sum-rule silhouettes, and then constructs thermal–visible object models. Finally, multiple object tracking uses blobs constructed in sensor fusion to output the trajectories. Results demonstrate the advantage of our proposed framework in obtaining better results for both image registration and tracking than separate image registration and tracking methods.     

基于互信息及蚁群算法的红外与可见光图像配准研究

红外与可见光传感器是图像系统中常用的两种传感器,对这两种传感器图像进行有效的融合,能够得到更加丰富的图像信息,并具有更高的可靠性,有利于提高对图像信息的分析和识别能力.快速准确地实现图像配准是图像融合的前提.为此,本文提出了一种基于互信息及蚁群算法实现红外与可见光图像配准的方法.     

被动声传感器网时延概率定位算法

声传感器测量目标发出的声波信号存在纯方位量测、时延较大的特点,通过对多个声传感器组网,可以实现对目标的定位和时延校准处理.提出了一种被动声传感器网时延概率定位的综合处理算法.首先,对多个传感器量测数据进行动态选择,选出测向线交角更接近90°的两个传感器量测数据进行交叉定位,获得目标初始位置;其次,进行时延校准处理,并重新确定测向线交角更接近90°的两个传感器量测数据进行交叉定位.获得新的目标初始位置估计;最后,利用概率定位对新的初始位置进行概率修正,进而获得目标较为准确的位置估计,形成航迹.仿真结果表明,此算法具有计算量小,实时性强,定位精度高的特点.     

三维传感器极大似然配准算法

基于测量噪声的影响,提出一种精确极大似然法(EML)。配准估计是通过求最大似然函数获得的。说明了EML准则包含两类部分可分变量(实际目标位置和传感器配准误差),提供了两步递归优化算法,并通过计算机仿真证明了极大似然算法用于多传感器配准处理的估计精度高,适用的范围广,解决了最小二乘配准算法中存在的局限性;当多个传感器相距较远时,该算法明显优于传统的最小二乘配准算法。     

增强现实系统及其关键技术研究

首先概要介绍了增强现实技术的概念、相关特性,指出了视觉一致性是增强现实系统最需要解决的问题;接着给出了增强现实系统的总体结构,概略介绍了各组成部分的功能以及对应的关键技术;然后在此基础上论述了关键技术中的显示技术和跟踪注册技术,并以跟踪注册为重点介绍了的增强现实系统基于跟踪器的方法、视觉跟踪方法和复合跟踪法等多种方案,并分析了不同方法不同实现的特性和应用场合,最后对增强现实的当前应用和未来趋势做出了概要总结.