Description: 复杂场景下运动目标的实时跟踪，在完成对目标模式匹配，目标运动估计和石台控制处理的基础上，对三个模块力加以综合，实现对运动目标的实时跟踪。-sports complex scene and real-time tracking of the target, the completion of the target pattern matching, Target Motion estimation and control of Shitai, on the basis of three integrated modules. Campaign to achieve real-time tracking of the target. Platform: |
Size: 5740544 |
Author:丁德锋 |
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Description: 针对双基阵提供的有偏方位角量测信息,对双基阵纯方位目标可观测性的必要条件及其Cramer-Rao下限
进行了理论推导.在此基础上,采用一种新的辅助变量方法对双基阵纯方位跟踪性能进行改进,并在可观测条件下对
目标进行了蒙特卡洛仿真实验.实验结果表明,新的辅助变量方法可以使参数估计精度大大提高,并且上述理论对制
定实际的跟踪策略或算法具有一定的参考价值
-For double-base matrix provided biased information azimuth measurement of double-base bearings-only target array may be a necessary condition for observability and Cramer-Rao lower limit of the theoretical derivation. On this basis, a new auxiliary variable method of double-base bearings-only tracking array to improve performance and can be observed under the condition of targets Monte Carlo simulation. The experimental results show that the new auxiliary variable method can greatly improve the accuracy of parameter estimation and the formulation of the above-mentioned theory tracking the actual strategy or algorithm has certain reference value Platform: |
Size: 54272 |
Author:顾东 |
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Description: 用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为S(t)（1） 统计特性的初值为
（2）观测误差是与和均不相关的白噪声序列，并且有
（3）观测数据存放在附加的文件中（单位：m）。
要求：分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求：
（1） 距离S(t)的最佳估计及估计误差，
（2） 距离S(t-5)的最佳平滑及估计误差，
（3） 距离S(t+5)的最佳预测及估计误差，
（4） 对结果进行分析讨论。
-By one observer from the t = 1 PST on a moving target tracking for distance measurement, assuming that the observation interval is one second, the radar that the distance between the moving target for the S (t) (1) the statistical characteristics of the initial condition (2) observational error is not associated with white noise sequence, and (3) observational data stored in the attached document (unit: m). Requirements: Analysis of the above-mentioned object, the establishment of the system model, constructed Kalman filter, programming terms, seeking: (1) distance from S (t) the best estimate and the estimation error, (2) distance from S (t-5) the most good smoothing and estimation error, (3) distance from S (t+ 5) the best prediction and estimation error, (4) the results analyzed and discussed. Platform: |
Size: 2048 |
Author:裴海波 |
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Description: 跟踪相关论文，《基于彩色图像的机器人视觉跟踪》《基于扩展卡尔曼滤波的主动视觉跟踪技术》《基于视觉和扩展卡尔曼滤波的位姿和运动估计新方法》《一种运动目标检测与跟踪快速算法的研究》-Tracking the relevant papers, based on the color image of the robot visual tracking , Extended Kalman Filter based on active vision tracking technology, Based on the visual and extended Kalman filter and the position and orientation of new motion estimation method , a moving target detection and fast algorithm for tracking of Study Platform: |
Size: 1433600 |
Author:王云 |
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Description: 关于视频的课件，内容全面，包括：视频的各种编码，目标检测，目标运动估计，目标跟踪，视频建模等。-On the video courseware, comprehensive, including: a variety of video encoding, target detection, target motion estimation, target tracking, video modeling. Platform: |
Size: 9079808 |
Author:陈平 |
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Description: Multiple-input multiple-output (MIMO) radar has been receiving increasing attention
in recent years from researchers, practitioners, and funding agencies. MIMO radar is
characterized by using multiple antennas to simultaneously transmit diverse (possibly
linearly independent) waveforms and by utilizing multiple antennas to receive the
reflected signals. Like MIMO communications, MIMO radar offers a new paradigm
for signal processing research. MIMO radar possesses significant potentials for
fading mitigation, resolution enhancement, and interference and jamming suppression.
Fully exploiting these potentials can result in significantly improved target
detection, parameter estimation, as well as target tracking and recognition
performance. Platform: |
Size: 7849984 |
Author:可难 |
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Description: 在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。-The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved. Platform: |
Size: 4096 |
Author:李隆基 |
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Description: This method is used for tracking wavelet/optical flow-based
detection for automatic target recognition in the following paper:
Dessauer, M. and Dua S. “Wavelet-based optical flow object detection, motion estimation, and tracking on moving vehicles” Platform: |
Size: 1355776 |
Author:MemoSergey |
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Description: 用matlab编写的GPS卡尔曼滤波跟踪算法，可用于GPS导航里目标跟踪估计等，非常好用！-Prepared using matlab GPS Kalman filter tracking algorithm can be used for GPS navigation in target tracking estimation, very easy to use! Platform: |
Size: 1024 |
Author:和平 |
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Description: 用MATLAB编写的单目标跟踪算法程序，采用了递归式算法，包括极大似然然估计，卡尔曼滤波，扩展卡尔曼滤波和无迹卡尔曼滤波，带有注释，易于理解。-Written with the MATLAB program single-target tracking algorithm, using recursive algorithms, including maximum likelihood estimation, Kalman filtering, extended Kalman filter and unscented Kalman filter, with comments, easy to understand. Platform: |
Size: 11264 |
Author:asd |
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Description: 统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法，
但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这
一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自
多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和
仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附
加的计算量小等优点。-Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc
used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking
by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same
source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The
theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with
low calculation load added and higher precision 。 Platform: |
Size: 246784 |
Author:minnie |
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Description: 本文讨论了小波神经网络在机动多目标跟踪中的应用,多目标跟踪就是主体为了维持对多个目标(客体)当前状态的估计而对所接收的量测信息进行处理的过程。以非线性大规模并行分布式处理为特征的神经网络可以解决传统的目标跟踪方法的难以解决的计算量组合爆炸问题以及需要确定机动目标的数学模型的问题, 将小波分析原理与神经网络相融合,提出了基于小波神经网络的目标跟踪方法来提高系统的学习能力、表达能力以及机动多目标状态的估计精度。-This article discusses the application of wavelet neural network in motorized multi-target tracking, multi-target tracking is the main measurement information received in order to maintain the current state of the multiple goals (guest) estimated processing. The nonlinear massively parallel distributed processing is characterized neural network can solve the traditional target tracking methods computational combinatorial explosion problem difficult to solve the mathematical model of the problem and the need to determine the maneuvering target, the principle of wavelet analysis and neural network fusion, target tracking method based on wavelet neural network to improve the system' s ability to learn, the ability to express and maneuvering multi-target state estimation accuracy. Platform: |
Size: 421888 |
Author:yaomeng |
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Description: 多目标跟踪的主程序，包含跟踪起始，数据关联，目标状态估计等过程-The main process of multi-target tracking, including track initiation, data association, target state estimation process Platform: |
Size: 123904 |
Author:sunmin |
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Description: 多无人机（Unmanned Aerial Vehicle，UAV）协同目标跟踪在军/民用方面有
着广泛而又迫切的应用需求和重要的理论研究价值，是目前多 UAV 系统自主控制
领域的一个重要研究方向。本文以 UAV 执行对地侦察打击任务为应用背景，针对
复杂环境中多 UAV 协作式跟踪地面移动目标问题，重点围绕目标状态融合估计和
观测航迹优化两项关键内容展开研究-Multi-UAV (Unmanned Aerial Vehicle, UAV) has a broad and collaborative target tracking application needs urgent and important theoretical research value in the military/civilian, is the multi-UAV system with independent control area is an important research direction. In this paper, ground reconnaissance UAV to perform combat tasks as application background, moving target problem for the complex multi-UAV collaborative environment to track the ground, the focus on integration of the target state estimation and optimization of the two key elements of the observed track a study Platform: |
Size: 1948672 |
Author:俞吉 |
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Description: The extended Kalman filter applied to bearings-only target tracking
is theoretically analyzed. Closed-form expressions for the state
vector and its associated covariance matrix are introduced, and subsequently
used to demonstrate how bearing and range estimation
errors can interact to cause filter instability (i.e., premature covariance
collapse and divergence). Further investigation reveals that
conventional initialization techniques often precipitate such anomalous
behavior. These results have important practical implications
and are not presently being exploited to full advantage. Platform: |
Size: 1933312 |
Author:Gomaa Haroun |
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Description: This article introduces the concept of maneuver strategy and its transition probability matrix, and then builds a target tracking
model with multi-motion and multi-maneuver strategy model based on these concepts in order to resolve the problem of
undesired tracking performance especially when unexpected maneuver strategy changes of target occurs. To achieve further
improvements in tracking accuracy, a self-adaptive algorithm for maneuver strategy transition probability matrix is proposed in
this article, by utilizing the compression ratio of the maneuver strategy error. Besides, considering that most of the observation
platforms are already able to provide multi-group measurements different sensors, information filtering is combined in this
article to fuse and integrate multi-group measurements, thus effectively improving estimation stability and ameliorating the
inevitable soaring estimation error through target’s unexpected changes of maneuver strategy-This article introduces the concept of maneuver strategy and its transition probability matrix, and then builds a target tracking
model with multi-motion and multi-maneuver strategy model based on these concepts in order to resolve the problem of
undesired tracking performance especially when unexpected maneuver strategy changes of target occurs. To achieve further
improvements in tracking accuracy, a self-adaptive algorithm for maneuver strategy transition probability matrix is proposed in
this article, by utilizing the compression ratio of the maneuver strategy error. Besides, considering that most of the observation
platforms are already able to provide multi-group measurements different sensors, information filtering is combined in this
article to fuse and integrate multi-group measurements, thus effectively improving estimation stability and ameliorating the
inevitable soaring estimation error through target’s unexpected changes of maneuver strategy Platform: |
Size: 483328 |
Author:Gomaa Haroun |
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Search - target tracking estimation