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本文提出了一种改进的多源约束聚类算法,以解决多传感器多目标跟踪(Multi-Object Tracking/Estimation, MOTD)问题。MOTD问题对应于在缺乏噪声和目标运动模型等先验信息的情况下,对多个传感器的量测数据进行聚类。针对现有算法对选定传感器量测敏感的问题,本文提出的算法首先根据选定传感器量测数据点的局部密度,对该传感器量测数据进行筛选排序;其次,对排序后的每一个量测数据点,计算和其他传感器量测的高斯核距离,每个传感器返回距离最小的数据点;最后计算在截断距离内的数据点的数量,当大于给定阈值时判定这些数据点为目标产生的量测,簇的中心(个数)即为目标的位置(个数)。实验结果表明,对比现有多源聚类算法,本文提出的算法在传感器目标检测概率较高的场景中聚类精度和聚类速度均有所改善。 相似文献
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毫米波雷达已成为车联网中的主流传感器之一,可用于交通场景的多目标跟踪。本文将毫米波雷达安装于道路上方进行交通目标跟踪,针对基于帧内DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类的多目标跟踪中,在该安装场景下多径噪点难以去除和纵向的交通目标点云难以区分的问题,提出了基于帧间DBSCAN聚类的毫米波雷达交通多目标跟踪方法。该算法使用多帧合并处理的方式,利用帧序特征用于解决多径噪点问题,并利用空间纵向分段的方法改善了原算法在纵向上目标区分度不足的缺点。本文通过六组不同的实际场景实验,证明了本方法在不同场景下,均相比原方法对跟踪结果有不同程度的改善。 相似文献
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本文简述一种基于主客观协调预测(COSE),实现多目标探测和测量的一种新方法。此系统由三部分组成。首先利用有效的客观数据,即对确定的目标位置和速度用统计方法确定一组子集解,其次利用主观推断式信息,即所摄取的目标区域和亮度构成的一个小子集,在此子集中,所摄取的图像与已知目标的亮度相似,如果只有一个目标信息是有效的,它就作为真正的目标,如果在这个小子集有多于一个的目标信息,则执行第三步,最小方位角判断,找出图像信息中在它本身和目标预测位置矢量之间成最小方位角的那个目标作为真正的 相似文献
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为了使多基雷达系统(MSRS)能动态地协调各部雷达的发射参数及其所获得的量测的使用,进而在系统资源有限的约束下达到更好的性能,该文提出一种针对多目标跟踪的MSRS聚类与功率联合分配算法。首先,该文推导了目标跟踪误差的贝叶斯克拉美罗下界(BCRLB)。然后,以最小化多目标总体跟踪误差的BCRLB为目标,建立了包含聚类方式和发射功率两个优化变量的代价函数,并用循环最小化算法和投影梯度算法对这个双变量优化问题进行了求解。最后,通过仿真实验验证了提出算法的有效性。 相似文献
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本文重点研究传感器网络中能源高效的多目标跟踪问题.根据轨迹相似性对跟踪目标聚类,利用组对象跟踪实现所有对象的跟踪,能够有效地减少传输能耗,延长网络寿命.由于测量误差、低采样率以及环境干扰,很难获取目标的精确位置,因此轨迹数据存在固有的不确定性.忽略这种不确定性会降低轨迹挖掘质量,从而影响目标跟踪.提出基于不确定性轨迹挖掘的组对象跟踪方法.轨迹挖掘阶段首先为所有跟踪目标建立马尔科夫链模型,然后给出一种新的不确定轨迹相似性的度量,最后给出不确定轨迹聚类算法UTK-means对目标分组.组对象跟踪阶段向基站周期性地更新组中心轨迹的位置.实验结果验证了本文方法具有较高的聚类质量和节能效率. 相似文献
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针对复杂背景下尺寸未知的红外弱小目标检测难题,一种基于聚类思想的红外弱小目标检测方法被提出。首先,利用小目标形态学特征对原始红外图像进行预处理,生成新的密度特征图。其次,使用改进的密度峰聚类算法对潜在候选目标进行粗定位。然后,针对潜在目标的局部候选集,采用加权模糊集聚类算法对局部候选集进行目标与背景区域的精细分割,利用目标与背景之间的差异性在增强目标的同时抑制虚警。最后,对处理后的局部候选集进行自适应阈值提取真实目标。实验结果表明,与7种对比算法相比,该算法对尺寸未知的小目标具有良好的鲁棒性和检测性能。 相似文献
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针对杂波环境下的多目标跟踪数据互联问题,该文提出基于全邻模糊聚类的联合概率数据互联算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering, ANFCJPDA)。该算法根据确认区域中量测的分布和点迹-航迹关联规则构造统计距离,以各目标的预测位置为聚类中心,利用模糊聚类方法,计算相关波门内候选量测与不同目标互联的概率,通过概率加权融合对各目标状态与协方差进行更新。仿真分析表明,与经典的联合概率数据互联算法(Joint Probabilistic Data Association algorithm, JPDA)相比,ANFCJPDA较大程度地改善了算法的实时性,并且跟踪精度与JPDA相当。 相似文献
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在数据挖掘中,针对聚类过程中数据存在的稀疏性问题,如果仍用传统的欧氏距离作为聚类指标,聚类的质量和效率将会受到一定的影响。受到信息论中KL散度的启发,文中提出一种基于Spark开源数据框架下利用KL散度的相似性度量方法,对目前使用的聚类算法进行优化。首先,通过预聚类,对数据的整体分布进行分析;然后,借助KL散度作为聚类的距离指标,充分利用数据集中元素提供的信息来度量不同数据集的相互关系,指导数据的聚类,在一定程度上改善了数据分布稀疏性的问题。整个过程基于Spark分布式数据处理框架,充分利用集群的能力对数据进行处理,提升数据处理的准确度和算法的时间效率;同时利用KL散度作为数据聚类距离指标,以充分考虑数据内部蕴藏的信息,使得聚类的质量得到了提升。最后通过一个实验来验证所提算法的有效性。 相似文献
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We devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of 1) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events and 2) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a predetermined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs operate with one another to ensure that only one CH (preferably the CH that is closes to the target) is active with high probability, (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with nonredundant, essential information to determine the target location, and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we use with the use of Voronoi diagram, the CH that is usually closes to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred. 相似文献
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Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter (CRPF) is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption. 相似文献
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为了从扫描图像序列中检测弱小运动目标并对其状态参数进行估计,提出一种基于随机有限集理论的目标联合检测跟踪算法.根据推扫型光学传感器的扫描特性,建立目标在像平面的运动模型和测量模型.将目标状态和量测数据描述为随机有限集合,将目标的联合检测跟踪问题建模为目标状态集的贝叶斯最优估计问题,并依据随机有限集理论推导出贝叶斯滤波的预测和更新表达式.从算法实现的角度,利用高斯混合技术实现算法的递推滤波.仿真结果表明,该算法适应杂波的能力强,对漏检的影响更小,可以有效完成推扫型光学传感器的目标检测跟踪任务. 相似文献
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由于水下传感器网络(Underwater Sensor Network,USN)的能量、带宽有限,传输原始量测数据前需要进行量化处理。面向目标跟踪,在传输比特数据量的约束下,提出了非短视量化比特分配算法。首先,推导了量化量测下的条件后验克拉美罗下界,并将其设为优化目标,建立了比特分配优化模型。在此基础上,提出了一种双层近似动态规划的算法来实现比特分配的优化,在所设时间窗内利用第一层近似动态规划分配各个时刻的比特,并利用第二层近似动态规划在各分支上实现水下传感器节点的比特分配,进一步提升了计算效率。仿真结果表明,所提算法在满足实时性的要求下具有更稳定的跟踪性能。 相似文献
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Vercauteren T. Dong Guo Xiaodong Wang 《Selected Areas in Communications, IEEE Journal on》2005,23(4):714-723
We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. In order to meet the requirements inherent to sensor networks such as distributed processing and low-power consumption, a collaborative signal processing algorithm is presented. At any time, for a given tracked target, only one sensor is active. This leader node is focused on a single target but takes into account the possible existence of other targets. It is assumed that the motion model of a given target belongs to one of several classes. This class-target dynamic association is the basis of our classification criterion. We propose an algorithm based on the sequential Monte Carlo (SMC) filtering of jump Markov systems to track the dynamic of the system and make the corresponding estimates. A novel class-based resampling scheme is developed in order to get a robust classification of the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC target tracking framework. Simulation results are presented to illustrate the excellent performance of the proposed multitarget tracking and classification scheme in a collaborative sensor network. 相似文献
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Time synchronization problem in underwater acoustic sensor networks (UWSN) was studied.Due to the propagation of acoustic signals in underwater environment and nodes movement bring some problems to time synchronization.A distributed time synchronization algorithm was proposed based on Doppler method,called NU-Sync.NU-Sync solved the problem of uncertainty propagation delay caused by nodes movement through calculating relative velocity.And autonomous underwater vehicle (AUV) was used as beacon node which can save energy consumption in the process of calculation clock skew.Simulation resulted show NU-Sync achieves high level time synchronization precision. 相似文献
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多机动目标跟踪问题是目前目标跟踪领域的一个重要研究方向,而数据关联与跟踪维持是多目标跟踪的核心部分。利用支持向量机在分类识别方面的优势,研究了基于支持向量机的数据关联方法。在此基础上,采用交互式多模型算法和无味卡尔曼滤波相结合的方法研究了多机动目标的跟踪问题。在该方法中,目标的运动状态和方位误差由选定的采样点来近似,在每个更新过程中,采样点随着状态方程传播并随非线性测量方程变换,得到目标的运动状态和方位误差的均值,避免了对非线性方程的线性化,至少给出最佳估计的二阶近似。与传统的扩展卡尔曼(EKF)方法进行了仿真比较,仿真结果表明了该算法的有效性。 相似文献