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1.
对一般的仅方位目标跟踪系统的可观测性进行了分析。这里"一般"的含义是指目标的运动假设突破了以往匀速直线运动的限制,扩展为更为一般的各种复杂运动。得出了这种条件下系统可观测的必要条件。在目标作匀速直线运动的假设下,仅方位目标跟踪的一个熟知结论是:系统可观测的必要条件是观测者必须机动。将这一结论推广到任意的目标运动条件,指出:在二维仅方位目标跟踪系统中,系统可观测的必要条件是观测者作比目标更为复杂的运动,并且独立方位数等于待定参数数。这一结论不仅有助于仅方位目标跟踪系统本质的认识,对于诸如跟踪算法开发和观察者机动策略优化等工程实践也有着十分重要的意义。  相似文献   

2.
夏佩伦  朱伟良 《电光与控制》2007,14(4):82-84,105
编队目标跟踪是多目标跟踪领域中的一个特殊问题.一般认为,在巡航状态下编队中的所有目标以同样的速度和航向匀速运动.这一事实可以用来帮助目标跟踪器改善跟踪效果.给出了在仅方位测量条件下编队目标跟踪的3种模型:分开模型、耦合模型和协作模型.分开模型实质上是多个单目标的同时跟踪,与单目标跟踪没有区别;耦合模型是将多个目标的待估状态耦合成一个综合的目标状态,各目标的测量信息也是合在一起使用;协作模型则是利用跟踪效果好的目标的速度和航向解算结果来帮助跟踪效果差的目标.对这3种方法进行的理论和仿真比较分析的结果表明,协作模型更有优势.  相似文献   

3.
针对机动目标跟踪问题,提出了一种引入径向速度量测的IMM滤波跟踪新方法.首先给出了观测数据的无偏坐标变换,由此得出位置坐标观测噪声的协方差矩阵.然后对引入径向速度量测的交互多模滤波跟踪算法进行了理论分析,最后进行了仿真实验.仿真结果表明,由于速度观测值提供了有关目标运动的更进一步的信息,从而使跟踪性能得到了较大的改善.  相似文献   

4.
针对以往光伏发电系统太阳能最大功率点跟踪(Maximun Power Point Tracking,MPPT)算法的跟踪速度及精确度不理想的特点,提出了一种新颖的变步长滞环比较法,对传统扰动观测法存在的速度和精度的矛盾进行优化。在Matlab/Similink下进行系统的建模与仿真,并进行实验分析。结果表明,该方法能够显著提高MPPT跟踪的速度和精度。  相似文献   

5.
为处理纯方位跟踪(BOT)中的非线性问题,提出了一种Unscented粒子滤波(UPF)跟踪方法.在使用Unscented变换的基础上,利用UPF来加入最新的观测量并产生非线性粒子滤波(PF)的建议分布.结合纯方位跟踪模型,推导了UPF应用的具体算法步骤,使用匀速运动和机动目标两个BOT仿真实例,与其它滤波器进行了仿真对比,分析了跟踪性能和误差.仿真结果表明,对于纯方位跟踪问题,UPF不仅解决了扩展卡尔曼滤波器的线性化损失难题,而且与PF等粒子滤波器相比,具有更高的跟踪精度.  相似文献   

6.
鲁棒的实时多车辆检测与跟踪系统设计   总被引:1,自引:0,他引:1  
场景中的运动阴影导致多目标粘连,车辆间的相互遮挡使得跟踪识别困难.本文针对这两个影响实时车辆检测与跟踪系统性能的主要因素,采用基于无偏卡尔曼滤波器(UKF)的方法为场景背景建模,提取出运动区域,再通过边缘特征检测出场景中的运动阴影,然后利用角点信息将目标与阴影分离;提出了一种基于运动预测框的目标跟踪算法,将它与基于车辆平行四边形轮廓的遮挡分割方法结合,构建了多车辆目标的实时跟踪系统,并用实验验证了它的实用性与鲁棒性.  相似文献   

7.
奚畅  蔡志明  袁骏 《电子与信息学报》2022,43(10):2805-2814
针对被动声呐方位-频率观测情况下粒子滤波检测前跟踪算法中高维采样效率低的问题,该文提出一种利用leg-by-leg机动可观测性特点的两级采样方法.首先,对leg-by-leg机动的可观测性进行分析;然后,建立极坐标系下的目标运动状态模型,以粒子相对观测站的距离和法向速度均匀分布为准则,提出将极坐标系下的目标状态向量映射至直角坐标系的方法;最后,为改善滤波收敛性,提出根据粒子的空间分布特征自适应地调整过程噪声协方差矩阵.仿真结果表明,对于典型的水下目标跟踪场景,所提方法可使滤波收敛率增大约47.6%,距离估计误差减小约329 m,滤波收敛时间缩短约450 s.  相似文献   

8.
针对固定单站平台,提出一种基于方位测量和速度估计的固定单站对运动目标定位及跟踪的模型算法。该模型算法利用多次的方位测量和对目标速度的估计来解算运动目标的位置航迹,再利用交互多模型滤波技术对目标航迹进行滤波跟踪和预测。仿真实验中利用卫星工具包 (STK)建模工具构建了典型场景,并分析了目标航迹的定位精确度及跟踪效果,对于位置固定的电子侦察系统和无源探测系统具有较为广泛的工程应用价值。  相似文献   

9.
赵力 《电子器件》2012,35(6):723-726
纯方位目标跟踪是一个重要的研究课题。它要解决的问题是:利用含有噪声的方位数据来估计目标的真实运动轨迹。本文提出了一种基于粒子群优化的改进粒子滤波算法。这种算法可以将最新的观测值引入观测估计,提高了预估精度,减少所需的粒子数,从而实现对目标真实运动轨迹更好的跟踪。  相似文献   

10.
跟踪起始与数据关联是多目标无源单站跟踪的关键技术.本文提出了一种基于目标多特征信息融合的自适应跟踪起始算法,通过构造多维动态可变的跟踪门,进行自适应跟踪起始检测,然后根据序列概率比检验准则进行轨迹确认.同时提出了一种基于多目标多特征信息融合的数据关联算法,首先通过定义多个特征数据关联度,将单个有效观测的多特征信息进行融合,再对多目标进行综合数据关联.计算机仿真表明,该跟踪起始算法能够快速有效地启动航迹,数据关联算法的性能要优于传统的NN方法和扩展的NN方法.  相似文献   

11.
为了提高光电跟踪仪对于高速运动目标的跟踪精度和稳定性,提出一种适用于光电跟踪仪的高速目标跟踪控制算法。利用光电跟踪仪、火炮、载体惯导系统、视频跟踪器和激光测距机输出的相关参数,通过一系列坐标转换、递推迭代和坐标反变换,完成瞄准线坐标系下方位速度环和俯仰速度环跟踪前馈补偿参数的计算,并将该参数分别叠加到方位、俯仰跟踪控制回路,参与跟踪控制;采用模拟航路进行验证,该跟踪控制算法对速度2.5 Ma的高速运动目标,跟踪系统误差和随机误差均小于0.15 mrad。实验结果表明,该方法能有效提高光电跟踪仪对高速运动目标的跟踪精度,响应速度快、动态滞后小。  相似文献   

12.
针对交互式多模型(IMM)算法切换滤波模型缓慢、跟踪精度低甚至发散的问题,提出了在机动目标跟踪中使用的高斯-艾肯特滤波算法。首先,该算法确定观测模型和滤波模型集,分别构造量测方程组和滤波方程组,形成总体观测矩阵;然后,针对跟踪目标的非合作机动,提出使用卡方检验来检验滤波效果,并通过滤波控制算法实时调整滤波内存长度,使用高斯-艾肯特滤波对机动目标跟踪具有很强的灵活性,实现自适应跟踪;最后,在目标跟踪仿真中与三种改进模型集的卡尔曼滤波IMM算法进行对比验证,对两类算法进行了复杂度分析。仿真结果证明了高斯-艾肯特滤波算法的有效性,在无先验信息条件下拥有更高的跟踪精度。  相似文献   

13.
针对运动目标跟踪,介绍了一种光电跟踪角速度、角加速度计算方法,给出了控制系统拉格郎日多项式插值跟踪算法误差分析,提出的伺服参数计算方法与分析,可为控制系统优化设计提供数据参考.  相似文献   

14.
Recently, Siamese trackers have received widespread attention for visual object tracking owing to their good balance between speed and performance. Those Siamese trackers heavily depend on target template while conventional practice fixes the template to initial frame. This strategy makes it unable to cope with variation of target appearance, which often leads to tracking failures and causes the gap in performance from other tracking methods. Despite the performance gain achieved by few template update methods with target templates generated by the tracked results, these tracked templates are easy to accumulate errors and cause tracking drift. In this paper, we propose two template update mechanisms to effectively adapt the target template during the tracking process which is dubbed as DTDU (Dynamic Template with Dual Update). Unlike predecessors that directly use the tracked template, we use initial template to perform similar transformation to the tracked template. Then the similar transformed template and the tracked template are combined linearly to capture the variation of target appearance. These updated templates are stored in a memory bank and retrieved to generate the final target template. In order to enhance quick update of memory bank to accommodate the target appearance, we use the retrieved template to further update the templates in memory bank for subsequent tracking. Extensive experiments on OTB-2015, VOT2016, VOT2018 and GOT-10k datasets have proved the effectiveness of these two update mechanisms and the proposed tracker achieves a real-time speed of 44 fps.  相似文献   

15.
应文  李冬海  胡德秀 《信号处理》2012,28(4):539-544
针对现有利用阵列单通道系统对机动目标跟踪精度不高,实时性差等不足,提出了一种新的基于改进粒子滤波算法的阵列单通道机动目标波达方向(direction of arrival,DOA)跟踪方法.该方法首先在利用接收机轮流采样建立数学模型的基础上,建立跟踪模型.然后,利用粒子群优化算法对马尔科夫链蒙特卡罗( Markov Chain Monte Carlo,MCMC)粒子滤波算法的重采样环节进行优化处理,给出了一种交互MCMC粒子滤波算法,该算法克服了传统粒子滤波算法粒子退化及样本贫化的固有缺陷.最后利用该算法求解跟踪方程,实现了实时DOA估计.理论分析与仿真结果表明,本文方法可实现基于阵列单通道的DOA跟踪与波束形成一体化,且能够处理相干信号,与标准粒子滤波和子空间类算法相比,收敛速度快,跟踪精度高.  相似文献   

16.
Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms.  相似文献   

17.
Recently, there has been a trend in tracking to use more refined segmentation mask instead of coarse bounding box to represent the target object. Some trackers proposed segmentation branches based on the tracking framework and maintain real-time speed. However, those trackers use a simple FCNs structure and lack of the edge information modeling. This makes performance quite unsatisfactory. In this paper, we propose an edge-aware segmentation network, which uses the complementarity between target information and edge information to provide a more refined representation of the target. Firstly, We use the high-level features of the tracking backbone network and the correlation features of the classification branch of the tracking framework to fuse, and use the target edge and target segmentation mask for simultaneous supervision to obtain an optimized high-level feature with rough edge information and target information. Secondly, we use the optimized high-level features to guide the low-level features of the tracking backbone network to generate more refined edge features. Finally, we use the refined edge features to fuse with the target features of each layer to generate the final mask. Our approach has achieved leading performance on recent pixel-wise object tracking benchmark VOT2020 and segmentation datasets DAVIS2016 and DAVIS2017 while running on 47 fps. Code is available at https://github.com/TJUMMG/EATtracker.  相似文献   

18.
最小跟踪目标对比度、最小跟踪目标尺寸、最大跟踪目标速度、跟踪精度、目标捕获概率和目标捕获时间等六个指标是衡量电视跟踪箱跟踪性能的重要指标。本文介绍了电视跟踪箱跟踪性能检测仪设计方案、工作原理和组成,包括针对电视跟踪箱的性能检测设计的、基于PC总线的模拟目标产生卡,该卡可以利用软件编程产生目标大小、对比度、运动轨迹、速度和方向等参数都可以精确调整的运动或静止模拟目标。该检测仪已应用于多种电视跟踪箱的性能测试和评价中,与传统的室内标志法相比,具有可编程性好、精度高、通用性强和运动状态可控性好等特点。  相似文献   

19.
Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without another bounding box; this makes it very challenging to accurately estimate the target state. Accordingly, in this paper, we address the above-mentioned problem by proposing a novel tracking method based on a distance-IoU (DIoU) loss, such that the proposed tracker consists of target estimation and target classification. The target estimation part is trained to predict the DIoU score between the target ground-truth bounding-box and the estimated bounding-box. The DIoU loss can maintain the advantage provided by the IoU loss while minimizing the distance between the center points of two bounding boxes, thereby making the target estimation more accurate. Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed. Comprehensive experimental results demonstrate that the proposed method achieves competitive tracking accuracy when compared to state-of-the-art trackers while with a real-time tracking speed.  相似文献   

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