基于特征在线选择的目标压缩跟踪算法

基于压缩感知理论的压缩跟踪算法能够有效地实现对目标的跟踪, 具有良好的实时性, 但该算法对目标特征没有进行在线选择导致跟踪鲁棒性不高. 本文提出一种基于特征在线选择的目标压缩跟踪算法. 首先, 在目标附近采样得到正负样本集合, 计算样本的多尺度矩形特征, 采用压缩感知中的随机投影矩阵对高维特征投影得到低维压缩域特征, 对压缩域特征进行在线选择提取最优特征, 剔除被污染的样本特征, 使用简单高效的朴素贝叶斯分类模型进行样本判断, 实现对目标的跟踪, 同时对跟踪中目标在摄像头中的尺度变化进行建模, 给出目标尺度变化的定量描述, 实现了适应目标尺度变化的多尺度跟踪. 实验结果表明本文算法具有更好的鲁棒性与更高的跟踪精度, 对目标跟踪中的遮挡、光线突变、尺度变化和非刚性形变等因素具有较好的抗干扰能力, 同时算法复杂度低, 可以满足实时性要求.     

基于压缩感知的多特征加权目标跟踪算法

针对被跟踪目标运动、纹理或环境变化时, 采用基于压缩感知目标跟踪算法目标易漂移、丢失的问题, 提出了改进的压缩感知目标跟踪算法。通过压缩感知算法提取灰度和纹理特征, 计算特征对样本分类结果并更新特征的权值, 使用加权过的特征寻找目标在下一帧的位置。对不同视频的测试结果表明, 提出的算法在目标运动、纹理或环境变化的情况下跟踪准确, 在目标大小80×120像素时平均帧速为25 fps。与传统的压缩感知跟踪算法和其他跟踪算法相比, 所提出的算法在目标运动、纹理或环境变化时能快速准确地获取跟踪目标, 并具有更强的鲁棒性。     

基于深度信息的压缩感知人脸检测跟踪

传统的压缩感知跟踪是基于彩色视频图像序列中的目标跟踪, 但在跟踪过程中可能会受到光照变化和旋转遮挡因素的影响, 从而导致复杂环境下跟踪结果的鲁棒性不足. 为了获得稳定的跟踪结果, 提出了一种基于深度信息的压缩感知人脸检测跟踪算法. 首先, 根据改进的质心分割算法确定首帧深度图中人脸的跟踪位置. 其次, 根据深度信息计算出深度图中每一点对应的平均曲率并形成平均曲率图. 然后, 基于平均曲率图随机提取压缩特征; 最后, 通过压缩降维, 目标邻域搜索, 迭代更新特征模板, 计算出平均曲率图中下一帧人脸的最优跟踪位置, 实现人脸跟踪. 实验结果表明, 将人脸的深度信息和压缩感知特征相结合在光照变化和旋转遮挡情况下具有很好的鲁棒性, 能更好的实现复杂背景下对多姿态人脸的跟踪.     

基于MHT的网络化雷达抗干扰算法

为解决分布式干扰下,单雷达目标发现性能较差、目标检测概率较低、航迹不连续以及雷达不能有效对目标进行跟踪的问题,提出基于MHT与秩K融合规则的网络化雷达抗干扰算法。对传统的MHT算法进行优化,缩短算法运行时间;对雷达干扰进行识别,针对分布式干扰运用优化后的MHT算法,计算每一个目标的发现概率;利用秩K准则实现融合后的目标检测。分布式干扰下网络化雷达模型对目标的跟踪实验验证了该算法的有效性。     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

基于压缩感知的实时手势检测和跟踪算法

基于计算机视觉的实时手势检测与跟踪算法是人机交互领域的一项关键技术,传统的手势检测与跟踪算法将检测和跟踪分成两个独立的模块进行,检测与跟踪结果受手势姿态变化、目标遮挡、运动模糊以及外界环境干扰等因素的影响。提出了一种基于压缩感知的实时手势检测和跟踪算法,将基于检测得到的手势信息与基于压缩感知跟踪算法得到的目标信息进行有效融合,从而实现有效的手势检测与跟踪,与传统算法相比,该算法能实现手势跟踪自动初始化和跟踪错误后自我恢复功能。实验结果表明,提出的算法能对手势运动进行快速、连续、准确的识别,满足人机交互的要求。     

基于压缩感知的监控视频多目标自适应跟踪算法

针对压缩感知跟踪算法无法自动识别目标和易积累噪声导致目标丢失的问题,提出一种融合图像预处理的压缩跟踪算法。算法在初始化阶段,采用背景建模和背景差分法实现自动识别目标区域和多目标的判定。随后,使用压缩跟踪算法进行多目标的跟踪。在跟踪过程中,采用更新机制,减少噪声的积累,防止目标丢失。更新背景模型,使算法适应光线变化。算法在监控视频库上进行测试,并与压缩跟踪算法进行了对比,实验结果表明,提出的多目标自适应跟踪算法,在主客观性能方面较同类算法具有显著提高,其跟踪精度和鲁棒性方面得到了改善。     

分布式并行粒子滤波算法结构分析与研究

粒子滤波器是解决非线性/非高斯系统状态估计的有效技术,广泛应用于目标跟踪、无线通信.自动控制等领域.但因其计算复杂、计算量庞大等缺陷,无法满足实时系统的应用需求.针对粒子滤波器计算量大、实时性差的问题,提出了一种基于MPI的分布式并行粒子滤波算法,给出了Master-Slave并行模式下任务分配、数据划分与负载平衡策略.实验结果表明,若忽略通信代价,加速比基本呈线性增长.     

基于分布式压缩感知的MIMO-OFDM系统信道估计研究

针对MIMO系统信道的联合稀疏特性,提出一种基于分布式压缩感知(DCS)的MIMO-OFDM系统信道估计方法。分布式压缩感知(DCS)被视为分布式信源编码和压缩感知(CS)的结合,论文详细论证了分布式压缩感知理论在MIMO-OFDM系统中运用的可行性。将该算法与基于压缩感知理论的Co SAMP算法做比较,仿真结果表明,基于DCS算法的信道估计不仅性能更优,而且可以实现更低的时间复杂度。     

一种基于预测策略的目标跟踪算法研究

移动目标跟踪是无线传感器网络中的一项重要应用,引起了越来越多的关注.采用静态网格网络结构,针对现有无线传感器网络目标跟踪算法不能兼顾精度和能耗的问题,提出了一种基于预测策略的目标跟踪算法.当目标进入监控区域后,节点携带的震动传感器感知到目标,簇头节点根据节点检测目标信号强度值来计算目标位置,目标位置计算采用一种基于检测...     

Cooperative distributed target tracking algorithm in mobile wireless sensor networks

The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...     

Collaborative signal processing for target tracking in distributed wireless sensor networks

Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.     

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.     

Distributed multirate interacting multiple model fusion (DMRIMMF) with application to out-of-sequence GMTI data

This note develops a distributed approach for fusing ground moving target indicator data with out-of-sequence (OOS) measurements. A multirate interacting multiple model (MRIMM) fusion algorithm is developed for effectively fusing multirate information. The multirate approach provides an excellent framework for efficient information retrodiction and forward update. A multirate interacting multiple model filter is employed locally to track a target with or without maneuvering behavior. The combination of global MRIMM fusion and local MRIMM tracking proves to be powerful for tracking and fusing maneuvering and nonmaneuvering targets in an environment of OOS measurement reporting.     

基于变步长序贯压缩的频谱快速感知算法

对信号稀疏度未知甚至时变的频谱感知应用 场景,将自适应思想与序贯压缩频谱感知技术相结合,提出了一种可变步长序贯压缩频 谱快速感知算法。新算法建立了下一次判决所需观测值数目步长因子与当前对数似然比距门 限距离之间的函数关系,克服了现有的序贯压缩检测算法以固定步长增加观测值的不足。分 别以分段函数和抛物线函数为步长因子调整规则进行理论分析和仿真实验。仿真结果表明： 与现有的序贯压缩检测算法相比,变步长算法具有检测速度快、观测值数目少和计算复杂度 小等优点。     

基于压缩感知的含旋转部件目标ＩＳＡＲ成像方法

在ISAR成像中,某些雷达目标的部件存在旋转运动,会对目标主体信息产生干扰导致目标成像质量下降,严重时甚至无法实现成像。本文结合压缩感知理论提出了一种含旋转部件目标成像方法。在成像时间内,由于目标主体部件相对于成像区域的位置保持不变,而旋转部件的位置在不断变化,因此,对回波信号运用压缩感知理论可得到目标主体部件的信息,从而有效剔除了旋转部件带来的影响并且大幅减少了回波数据量。最后仿真结果验证了该方法的有效性,并对其抗噪性能进行了分析。     

Robust fusion steady‐state filtering for multisensor networked systems with one‐step random delay,missing measurements,and uncertain‐variance multiplicative and additive white noises

The robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, one‐step random delay, missing measurements, and uncertain noise variances, the phenomena of one‐step random delay and missing measurements occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, derandomization approach, and fictitious noise approach, the original multisensor system under study is converted into a multimodel multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. In addition, by using the proposed model transformation approach, the centralized fusion system is obtained, furthermore the robust centralized fusion steady‐state Kalman estimators are proposed. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of nonnegative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.     

基于贪婪重建的射频传感器网络稀疏目标跟踪

在射频传感器网络中利用接收信号强度来实现无设备运动跟踪是一种新兴技术。由于待重建的场景是稀疏的,即目标个数很少,这样可以应用压缩传感技术。提出把目标跟踪作为压缩传感的信号重建问题,并提出贪婪检测估计算法来求解目标位置。同时,设计一种利用目标先验位置信息的反馈跟踪方法来减少信号重建时所需的测量数。通过实验验证：贪婪检测估计算法可获得较好的信号重建结果,而且在目标跟踪应用中也较为精确。     

恶意攻击下基于分布式稀疏优化的安全状态估计

恶意生成的量测攻击信号是导致信息物理系统(Cyber-physical system,CPS)探测失效的主要原因,如何有效削弱其影响是实现精准探测、跟踪与感知的关键问题.分布式传感器网络(Distributed sensor network,DSN)依靠多传感器协作与并行处理突破单一监测节点的任务包线,能够显著提升探测...     

基于量化新息的容积粒子滤波融合目标跟踪算法

针对现有非线性网络化目标跟踪融合算法存在的精度低和实用性差等不足,以一类带有噪声相关的非线性网络化目标跟踪系统为对象,研究基于测量新息量化策略和容积粒子滤波（Cubature particle filter,CPF）的目标跟踪融合算法. 首先,利用状态方程恒等变换和矩阵相似变换理论解除过程噪声与测量噪声以及测量噪声之间的相关性;其次,各个传感器节点采用自适应策略量化局部测量新息并将其发送到融合中心（Fusion center,FC）;随后,在集中式融合框架下采用容积粒子滤波器设计基于测量值扩维的量化融合跟踪算法,进而给出相应的顺序滤波量化融合算法,上述算法可有效解决因自适应量化引起的非高斯问题;最后,通过两个计算机仿真实验验证了所提出跟踪算法的有效性.