相控阵雷达外弹道测量事后数据处理软件设计

由于多径回波信号的干扰,雷达对低空掠海飞行小目标的实时测量很难达到靶场外弹道测量的精度要求;根据靶场应用需求,采用四阶差分统计误差计算方法,对相控阵雷达中心控制计算机实时处理过程中已经录取下来的测量数据进行随机误差修正处理,并且将结果用图形的方式显示;文中给出某次飞行试验的数据处理结果,通过与实时测量数据结果比较表明,该软件可较好地修正测量随机误差,提高数据处理精度,满足靶场雷达事后数据处理的要求,为靶场试验决策提供依据.     

相控阵测量雷达扫描特性及误差补偿方法研究

基于相控阵雷达工作原理,分析了天线扫描对波束宽度和天线增益的影响;以往只考虑相控阵雷达电扫法线跟踪测量精度,未考虑相控阵雷达偏轴电扫跟踪测量精度;通过雷达步进偏轴跟踪模拟目标方法,采集雷达角度补偿数据库,实现了偏轴扫描跟踪角度误差补偿;试验结果表明,该补偿方法能够提高相控阵雷达偏轴扫描跟踪坐标测量精度。     

基于决策距离的分布式多传感器动态分组算法

飞行试验中多传感器测量系统中因类型、精度以及空间位置的变化,导致在数据融合时往往不能得到最优测量精度;提出了一种新的分布式多传感器目标跟踪分组融合算法,即利用模糊理论中的决策距离(Decision Space)思想,对飞行试验目标跟踪的多传感器系统进行动态分组(Dynamic Grouping),通过定义多传感器之间的关系矩阵(Relation Matrix),依据判别门限(Threshold)判定其是否参与最终处理,据此以获得在分布式多传感器目标跟踪测量系统中目标跟踪测量的最佳融合数据精度;仿真结果证明,该算法是一种有效的分组算法.     

雷达导引头速度和角度跟踪互助算法设计

主动雷达导引是现代导弹的主流研究方向,而角度跟踪算法和速度跟踪算法则是主动雷达跟踪算法中的重要组成部分,其跟踪精度决定着雷达系统的制导精度。但是,当弹载雷达受到速度拖引干扰时,速度通道测量值就不再可信。为了在系统截获目标阶段,或者在目标跟踪阶段及时判断出速度拖引,文中进行了角度跟踪和速度跟踪的算法设计,解决了干扰环境下的跟踪稳定性问题,实现了算法软件在系统的验证试验,仿真及内场试验证明其算法具有可行性和有效性。     

基于高频地波雷达的无角度双站雷达目标跟踪算法

高频地波雷达对于远距离目标难以给出准确的方位角观测信息,从而严重影响超视距目标的跟踪精度。为此,提出一种基于高度参数化扩展卡尔曼滤波（HPEKF）的无角度双站雷达目标跟踪算法,仅需要2部雷达得到距离和径向速度信息。通过 HPEKF技术可以对目标进行三维定位与跟踪,忽略雷达量测的方位角信息,较好地弥补当目标距离较远时测量角度精度不够精确的问题,扩展了算法的应用环境。仿真实验结果验证了该算法的可行性和有效性。     

传感器自适应跟踪算法研究及仿真系统实现

雷达和红外作为目标跟踪常用的两种探测手段,各有其优缺点,利用雷达高精度的距离测量和红外高精度的角度测量,通过信息融合技术充分实现二者的优势互补,并结合交互式多模型(IMM)跟踪思想,给出对目标位置的精确估计;设计基于雷达/红外多传感器跟踪平台的自适应融合跟踪算法,实现根据目标不同运动特性进行跟踪模型灵活、合理切换的自适应目标跟踪,改善对目标的综合识别,达到更好的跟踪效果;选取当前工程实践中广泛应用的目标运动模型,设计基于VC++环境的目标跟踪仿真系统软件,并利用MFC界面制作技术创建可视化目标跟踪仿真软件平台,对跟踪算法性能进行验证。     

雷达自动跟踪测量的C2算法抗多径干扰研究

将传统的多目标分辨算法（C^2算法）应用于低角多径环境下目标俯仰角的跟踪测量,并在不同多径反射环境下对不同高度、不同飞行速度和飞行力。向的目标进行了仿真,得到良好的仿真结果,表明该算法具有比较优良的抗多径干扰性能,通过对仿真结果的分析。验证了该算法在低空多径环境下目标俯仰角测量的有效性;将该方法席用于靶场雷达系统低空目标俯仰角的跟踪测量,可较大地提高测量精度。改善靶场测控系统的不足。     

多基准站GPS定位靶场测试方法研究

在靶场试验过程中,传统的单基准站由于基线长度的限制使其作用范围有限,从而超出一定距离后,差分精度随着大气误差相关性的减弱而明显受到影响;为了解决靶场GPS单频伪距差分定位距离远时精度下降问题,提出了多基准站GPS定位伪距差分定位方法;该方法深入分析伪距方程和伪距差分定位之间的关系;建立靶场基准站网络实时接收目标定位数据和基准站定位数据并实时传送到控制中心;利用基于距离的线性内差模型,设计多基准站数据处理算法和多基线测量数据加权处理方法,实时解算目标定位信息;测试结果表明,多基准站GPS定位伪距差分定位方法,能够有效地解决常规单基准站伪距差分定位精度随用户站与基准站距离增加而降低的问题,同时提高了测试精度。     

雷达低仰角偏轴跟踪测量偏差补偿算法的改进研究

由于多径回波信号的干扰,严重影响了单脉冲测角体制雷达对低仰角目标俯仰角的测量,并使其对低仰角目标俯仰角的闭环跟踪测量实现极为困难;通过对多路径反射环境模型分析,同时考虑镜面反射和漫反射的干扰,得出了单脉冲雷达低仰角跟踪时俯仰角测量误差的产生原因,用传统的多目标分辨算法(C~2算法)弥补偏差补偿算法在目标飞行高度很低(5 m以下)时和距离近时补偿效果较差的不足;在给定的测量环境下对不同高度目标进行了仿真,得到良好的仿真结果,表明两种算法结合使用,可较大地提高低仰角目标偏轴跟踪俯仰角的测量精度。     

基于HLA信号级火控雷达目标跟踪系统仿真

目标跟踪作为火控雷达的一种重要工作模式,同时是火控系统的首要任务.本文基于HLA构建了可扩展、可重用的火控雷达信号级目标跟踪仿真系统.依据火控雷达闭环、连续跟踪与自动测量特点,从信号级仿真出发,通过火控雷达跟踪系统中天线模块、接收机与信号处理模块、控制系统模块的有机结合,完成动态目标跟踪仿真过程.结果表明仿真系统能逼真有效完成火控雷达目标跟踪仿真,对火控雷达训练模拟系统设计与雷达电子战系统评估研究中有一定的参考价值.     

激光跟踪测距仪在体育领域中的应用

介绍了激光的特点及激光跟踪测距仪的工作原理 ,在此基础上 ,设计了一种激光跟踪测距系统 ,对该系统的组成及主要部件进行了阐述 ,给出了该系统的主要性能数据 ,并对该系统在体育领域中的应用前景作了展望。     

Minimax particle filtering for tracking a highly maneuvering target

In this paper, we propose a new framework of particle filtering that adopts the minimax strategy. In the approach, we minimize a maximized risk, and the process of the risk maximization is reflected when computing the weights of particles. This scheme results in the significantly reduced variance of the weights of particles that enables the robustness against the degeneracy problem, and we can obtain improved quality of particles. The proposed approach is robust against environmentally adverse scenarios, particularly when the state of a target is highly maneuvering. Furthermore, we can reduce the computational complexity by avoiding the computation of a complex joint probability density function. We investigate the new method by comparing its performance to that of standard particle filtering and verify its effectiveness through experiments. The employed strategy can be adopted for any other variants of particle filtering to enhance tracking performance.     

A tracking loop based on tightly coupled range and velocity filter equations

A joint range-velocity closed tracking loop,which is based on tightly coupled range and velocity filter is proposed.When the measured velocity value is adopted in the range tracking loop to modify the velocity and acceleration equations from traditional α-β-γ filter,the tracking loop based on tightly coupled range and velocity filter can not only track the range and the velocity simultaneously,but also improve the range tracking accuracy.The experimental results show that the tracking errors about range thermal noise in the proposed loop is lower than those in the traditional loop over 2.2 dB,when filter parameters satisfy least mean-square error criterion.Moreover,with the increase of the filter parameter,the tracking performance of our schemes are improved accordingly.     

Use of wrist mounted range profile scanners for real-time tracking

The results of experiments in realtime tracking of a moving object using wrist mounted laser range finders are presented. The objective is to have the robot endeffector maintain a constant pose with respect to a flat object moving in three-dimensional space in the field of view of the range finders. Tracking is done with maximum linear and angular speeds of the target object being 25 cm/s and 0.5 rad/s, respectively. The software is designed to ensure that the robot has smooth motion, without any sharp discontinuities. A performance study of the tracking experiment is presented.NRC #33164     

基于正交试验的运动目标跟踪算法性能评价

针对目前运动目标跟踪算法性能评价中测试数据量大、试验次数多以及未充分考虑多因素组合场景下的算法性能表现等问题,提出一种基于正交试验的运动目标跟踪算法性能评价方法。分析影响算法性能的因素和水平,构建正交试验数据集,通过该数据集测试算法性能并利用极差分析法分析数据结果,以得到各影响因素间的强弱关系以及算法性能表现较好时的因素水平组合方式。分析结果表明,该方法能够全面、有效地评估运动目标跟踪算法的性能,减少测试次数和数据量,并为其他图像处理算法的性能评估提供参考。     

Automatic calibration of laser range finder positions for pedestrian tracking based on social group detections

Laser range finders (LRF’s) are non-invasive sensors which can be used for high-precision and anonymous tracking of pedestrians in social environments. Such sensor networks can be used in robotics to assist in navigation and human–robot interaction. Typically, multiple LRF’s are used together for such tasks, and the relative positions of these sensors must be precisely calibrated. We propose a technique for estimating relative LRF positions using observations of social groups in the pedestrian flow as keypoint features for determining coarse estimates of relative sensor offsets. The most likely offset is estimated using a generalized Hough transform and used to identify sets of possible shared observations of individual pedestrians between pairs of sensors. Outliers are rejected using the RANSAC technique, and the resulting shared observations from each sensor pair are combined into a constraint matrix for the sensor network, which is solved using least-squares minimization. Results show calibration accuracy of sensor positions within 34?mm and 0.51°, and an analysis of pedestrian data collected from ubiquitous networks in three public and commercial spaces shows that the proposed calibration technique enables pedestrian tracking within 11?cm accuracy.     

Gaussian mixture presentation of measurements for long-range radar tracking

In long-range radar tracking, the measurement uncertainty region has a thin and curved shape in Cartesian space due to the fact that the measurement is accurate in range but inaccurate in angle. Such a shape reflects grievous measurement nonlinearity, which can lead to inconsistency in tracking performance and significant tracking errors in traditional nonlinear filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we propose a modified version of the Gaussian Mixture Measurement-Integrated Track Splitting (GMM-ITS) filter to deal with the nonlinearity of measurements in long-range radar tracking. Not only is the state probability density function (pdf) approximated by a set of Gaussian track components, but the likelihood function (LF) is approximated by several Gaussian measurement components. In this way, both the state pdf and LF in the proposed filter have more accurate approximation than traditional filters that approximate measurements using just one Gaussian distribution. Simulation experiments show that the proposed filter can successfully avoid the inconsistency problem and also obtain high tracking accuracy in both 2-D (with range-angle measurements) and 3-D (with range-direction-cosine measurements) long-range radar tracking.     

基于LoRa信号的行走距离和方向估计

行走追踪在真实场景中具有广泛应用,可以用于安防监控、老人看护、室内导航等场景.近年来,基于无线射频信号的非接触式行走追踪受到了研究人员的广泛关注,包括基于Wi-Fi信号、RFID信号等的行走追踪系统.然而,现有的行走追踪系统依然面临感知范围小、感知设备部署稠密等问题.在本文中,我们首次将用于物联网低功耗、远距离通信的LoRa技术应用到非接触式的大范围行走追踪中,显著地增加了行走追踪系统的感知距离.特别地,通过利用LoRa网关上配置的多天线,利用两根天线上接收信号的比,可以有效地消除噪声干扰以及收发不同步带来的误差,从而进一步提升了感知范围,然后利用计算切线向量相位变化的方法准确计算原始信号中动态分量的相位变化来在估计行走距离和方向.基于此,本文提出基于LoRa的非接触感知系统,可以在一段自然连续的行走活动中准确地判断人的动静状态并切割出行走片段,进而计算出行走的距离和方向,实现人的行走追踪.实验验证了系统计算行走距离和方向的准确性和实时性,其中距离计算的平均误差为3.8%,准确判断行走方向所需时间为0.7 s.