Joint tracking and classification with constraints and reassignment by radar and ESM

We consider the problem of joint tracking and classification using the information from radar and electronic support measure. For each target class, a separate filter is operated in parallel, and each class-dependent filter is implemented by interacting multiple model regularized particle filter. The speed likelihood for each class is defined using a priori information about speed constraint and combined with the likelihoods from two sensors to improve tracking and classification. Moreover, the output of classifier is also used for particle reassignment of different classes, which might lead to better performance. Simulations show that our proposed method can provide reliable tracking and correct classification.     

基于复合动态模型和证据融合架构的移动物体检测与跟踪方法

针对现有方法中移动物体检测与跟踪的准确性精度较低的缺点,提出一种基于多传感器检测分类的移动物体描述和感知方法：建立了一个包含核心对象动态特征和分类描述的复合模型,在此基础上设计了一个基于证据框架的信息感知与融合方法,通过整合动态模型和不确定性特征来实现对移动物体的检测和跟踪。为了验证所提方法的有效性,在一辆安装有雷达、激光雷达和摄像头的演示车上进行了相关实验,在不同驾驶场景下针对行人、卡车和轿车三个移动物体进行了检测与跟踪,实验结果证明所提方法具有非常高的准确性。     

基于似然域背景差分的行人检测和跟踪方法

基于激光雷达的室内机器人行人检测、跟踪容易受到复杂背景的影响。针对这种情况,提出一种基于似然域背景差分的行人检测、跟踪和跟随系统。利用即时定位与地图构建算法获得陌生环境的二维栅格地图,通过蒙特卡洛定位获得机器人在地图中的后验位姿,利用似然域模型分割出前景对应的激光雷达数据后,进行行人的检测、跟踪以及跟随。实验结果表明,该系统使行人检测准确率提升3.49%,平均检测时间缩短近32%,有效降低复杂背景对多行人检测与跟踪的影响,实现机器人对目标行人的实时跟随。     

基于多模型算法的低空机动目标跟踪研究

针对地面防空警戒和跟踪雷达系统机动目标的跟踪,将适用于常速运动模式的常速模型,常加速运动模式的常加速模型和转弯运动模式的转弯模型组成模型集,使用交互式多模型算法对机动目标进行跟踪。仿真结果表明,其中的两种组合性能很好,不仅能够精确跟踪到低空机动目标的运动形态,而且具有很好的收敛特性和稳定性,具有一定的工程应用价值。     

一种基于树形结构融合的目标跟踪算法

针对空中机动目标,利用目标多普勒信息和红外辐射信息建立具有树形结构的红外雷达跟踪系统状态估计模型.基于无迹卡尔曼滤波方法,提出一种自适应双波段红外并行融合算法,并基于红外融合结果,采用序贯滤波融合方法,与雷达传感器实现深层交互多模型融合估计.通过仿真表明了所提出的方法具有更小的距离跟踪误差和良好的跟踪精度.     

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.     

基于消息传递的机载雷达组网航迹融合

机载雷达组网航迹融合需要解决目标跟踪、数据关联与航迹管理3个子问题, 然而这3个子问题相互耦合,采用开环序贯估计算法会导致性能下降. 本文提出了一种基于消息传递的机载雷达组网航迹融合方法, 该方法在联合优化框架下解决目标跟踪、数据关联与航迹管理3个子问题. 首先, 建立机载雷达组网航迹融合的联合概率密度函数, 并将其转换为因子图. 其次, 将因子图分解为置信传播区域与平均场近似区域. 目标运动状态的统计模型服从共轭指数模型, 因此采用平均场近似以获得简单的消息传递更新公式. 数据关联包含一对一约束, 因此采用置信传播. 目标存在状态同样采用置信传播, 以获得更好的近似结果. 最后, 可以通过闭环迭代框架近似估计后验分布, 从而有效处理目标跟踪、数据关联与航迹管理之间的耦合问题. 仿真结果表明, 所提算法的性能优于多假设跟踪算法和联合概率密度关联算法.     

Efficient Particle Swarm Optimized Particle Filter Based Improved Multiple Model Tracking Algorithm

To meet the requirements of modern radar maneuvering target tracking system and remedy the defects of interacting multiple model based on particle filter, noninteracting multiple model (NIMM) and enhanced particle swarm optimized particle filter (EPSO‐PF) are proposed. The improved maneuvering target tracking algorithm (NIMM‐EPSO‐PF) in this article combines the advantages of NIMM with those of EPSO‐PF. NIMM is used to figure out the index of particles to avoid the high computing complexity resulting from particle interaction, and EPSO‐PF can not only improve the equation of particle update through the rules individuals develop an understanding of group but also enhance particle diversity and accuracy of particle filter through the small variation probability of superior velocity. Besides, the random assignment of inferior velocity is capable of upgrading filter efficiency. As shown by the experimental result, the NIMM‐EPSO‐PF not only improves target tracking accuracy but also maintains high real‐time performance. Therefore, the improved algorithm can be applied to modern radar maneuvering target tracking field efficiently.     

机载雷达跟踪方法研究与仿真

研究机载雷达跟踪运动物体的准确性问题.针对当物体运动的速度较快时,在机载雷达跟踪过程中需要完成光机扫描、图像合成、图像处理、信息传输等一系列综合处理过程,产生时间滞后,一旦被跟踪的物体运动速度较大,两者速度不匹配,就会造成跟踪高速运动目标识别不准确的问题.为了解决上述问题,提出基于滞后时间补偿的机载雷达跟踪方法.通过建立被跟踪的目标的运动模型,估计出跟踪物体下一刻运动位置,提前消除时间滞后,克服传统方法的缺点.实验结果表明,运用改进方法可以很好的消除时间上的滞后延迟,完成机载雷达对高速运动物体的跟踪,大幅提高跟踪的准确性.     

基于多传感融合的路面机动目标跟踪

Maneuvering targets tracking is a fundamental task in intelligent vehicle research. This paper focuses on the problem of fusion between radar and image sensors in targets tracking. In order to improve positioning accuracy and narrow down the image working area, a novel method that integrates radar filter with image intensity is proposed to establish an adaptive vision window. A weighted Hausdorff distance is introduced to define the functional relationship between image and model projection, and a modified simulated annealing algorithm is used to find optimum orientation parameter. Furthermore, the global state is estimated, which refers to the distributed data fusion algorithm. Experiment results show that our method is accurate.     

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。     

Hybrid state estimation: a target tracking application

In this paper we present a framework in which the general hybrid filtering or state estimation problem can be formulated. The problem of joint tracking and classification can be formulated in this framework as well as the problem of multiple model filtering with additional mode observations. In this formulation the state vector is decomposed into a continuous (kinematic) component and a discrete (mode and/or class) component. We also suppose that there are two types of measurements. Measurements that are related to the continuous part of the state (e.g. bearing and range measurements in a radar application) and measurements that are related to the discrete part of the state (e.g. radar cross-section measurements). We will derive an optimal filter for this problem and will show how this filter can be implemented numerically.     

Coherent processing for ISAR imaging with sparse apertures

To implement target detection,tracking and imaging in a multifunctional radar system,the wideband measurements for inverse synthetic aperture radar(ISAR)imaging are usually sparsely recorded.Considering the incoherence problem in such sparse-aperture ISAR(SA-ISAR)systems,we concentrate on the study of a coherent processing method in this work.Based on an all-pole model,the incoherence parameters between abutting sub-apertures can be effectively estimated.After coherence compensation,an optimization-based SAISAR imaging approach is provided from the view of statistics.Simulation and real data experiments validate the feasibility and effectiveness of the proposals.     

基于自适应脉冲间隔的火控雷达目标跟踪技术

为了能在提高雷达生存能力的同时,保持雷达的跟踪能力,提出一种基于自适应脉冲间隔的目标跟踪技术。给出火控雷达的信号模型及其改进的时域信号,分析基于匀加速运动模型的扩展卡尔曼滤波算法,研究脉冲间隔的自适应变化规律及信号的截获概率。仿真结果表明,该技术能准确跟踪目标,延长信号截获时间,从而降低侦察系统对火控雷达的截获概率。     

Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model

The trajectory of a shipbome radar target has a certain complexity, randomness, and diversity. Tracking a strong maneuvering target timely, accurately, and effectively is a key technology for a shipbome radar tracking system. Combining a variable structure interacting multiple model with an adaptive grid algorithm, we present a variable structure adaptive grid inter- acting multiple model maneuvering target tracking method. Tracking experiments are performed using the proposed method for five maneuvering targets, including a uniform motion - uniform acceleration motion target, a uniform acceleration motion - uni- form motion target, a serpentine locomotion target, and two variable acceleration motion targets. Experimental results show that the target position, velocity, and acceleration tracking errors for the five typical target trajectories are small. The method has high tracking precision, good stability, and flexible adaptability.     

改进稀疏表示模型的目标跟踪

针对受到光照、遮挡及姿态变化等引起的目标外观发生变化时,目标跟踪的鲁棒性和准确性较差的问题,将稀疏表示引入到粒子滤波框架进行目标跟踪,提出一种稀疏协同模型。首先,在目标运动定位模型中,使用灰度强度值表示目标对象;其次,判别模型通过训练正负模板集获得最优分类特征,并在生成模型中对目标直方图加权以提高目标生成效率;然后,将分类判别模型和生成模型集成在协同模型中,利用重构误差确定目标;最后,通过各模块独立更新,减少目标外观变化对目标跟踪的影响。实验结果表明,所提方法的平均中心误差仅为7.5像素,且具备良好的抗噪性和实时性。     

Kalman filter and joint tracking and classification based on belief functions in the TBM framework

The paper develops an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). The TBM model is identical to the classical model except all probability functions are replaced by belief functions, which are more flexible for representing uncertainty. It is felt that the tracking phase is well handled by the classical Kalman filter but that the classification phase deserves amelioration. For the tracking phase, we derive a minimal set of assumptions needed in the TBM approach in order to recover the classical relations. For the classification phase, we distinguish between the observed target behaviors and the underlying target classes which are usually not in one-to-one correspondence. We feel the results obtained with the TBM approach are more reasonable than those obtained with the corresponding Bayesian classifiers.     

移动机器人扩展卡尔曼滤波定位与传感器误差建模

针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.     

雷达地杂波建模与仿真实现

地面雷达设备在探测和跟踪目标时,会受到杂波的干扰,特别是雷达的俯仰角较低时,受地物杂波干扰更为严重,虽然现代雷达采用动目标显示等技术来抑制地物杂波,但其影响仍然存在.在研究地杂波仿真技术的基础上,提出了采用数字高程模型和杂波模型相结合的思想,重点对地杂波进行仿真.