A multi - target data association approach for road vehicle tracking 一种用于道路车辆跟踪的多目标数据关联方法
The fuzzy probabilistic data association of multiple targets tracking is presented in this paper , which define a target ? uzzy set on the measurement set at time k and then use fuzzy least mean square error method to estimate target states 给出了一种多目标跟踪的模糊概率数据关联方法,该方法在k时刻的回波集上定义一个目标模糊集,表示回波与目标之间的模糊关系。然后基于目标模糊集,利用模糊最小均方误差估计方法对目标状态作出估计。
By employing the state - space model for pulse trains , kalman filter and probabilistic data association , the thesis develops a deinterleaving algorithm for tracking mode in chapter 5 . the interacting multiple models ( imm ) approach is implemented to simultaneously estimate the pulse train pris of multiple emitters 论文第五章引入了基于卡尔曼滤波和概率数据关联方法进行脉冲列分析和去交错的研究,为辐射源跟踪状态下的去交错技术提供了理论依据和评估标准。
The methods of data association and tracking beginning and ending to single and multiple targets tracking in the multi - echo environment is listed . at the end of the thesis , a method is introduced , which is that based on the most closed principle , without the chosen echo , the current forecasting values added yawp based upon the former state values is considered as the target state estimated value . the value is an input of observation equation , the output of the observation equation is considered a chosen echo . and the method is validated in the simulation results 针对多目标跟踪问题,首先对多目标跟踪的原理和跟踪门的形成方法进行了概述,并对多回波环境下单目标跟踪和多目标跟踪的常用的数据关联方法和跟踪起始、跟踪终结方法进行了介绍,在本文的后半部分,对多目标的运动状态进行了仿真研究,提出了一种目标状态估计方法,该种方法的思想是当前时刻如果目标跟踪门内没有所期望的候选回波,首先计算出目标在前一时刻的运动状态下对当前时刻的预测值,并将该值叠加上系统噪声作为量测方程输入值,然后将观测值作为候选回波对目标进行状态估计。
In this paper , we describe the study background , meaning and methods of passive acoustic detective network , summarize the basic theories and methods of target tracking and data association , analyze some tipical data association algorithms include the nearest neighbor algorithm ( nn ) , probabilistic data association filtering ( pdaf ) , joint probabilistic data association filtering ( jpdaf ) , multiple hypothesis tracking ( mht ) , and multidimensional s - d assignment algorithm . 2 . in detective network , sometimes a surveillance region have only single sensor 从整体上描述了无源声音探测网络的研究背景、意义、基本框架和研究方法,概述了目标跟踪与数据关联的基本理论与方法,重点分析了几种典型的数据关联方法,包括最近邻方法、概率数据关联滤波器( pdaf ) 、联合概率数据关联滤波器( jpdaf ) 、多假设跟踪( mht )以及多维s - d分配算法。
数据: data; record; information关联: be connected; be related方法: method; means; way; techniqu ...关联方: affiliate; related party联合概率数据关联算法: jpda关联方程: tie-in equation关联方交易: related party transaction关联方披露: related party disclosure关联方向: associative direction健康和环境数据关联分析和监测项目: health and environment data linkage analysis and monitoring project内联方法: inline method数据关系级: data relationship level数据关系图: database diagram; databasediagram关联方向找寻器: correlation direction finder矢量数据关系格式: vector relational format关联方向追纵和范围系统: correlation qrientation tracking and range system关联数据: associated data关联数据库: relational database关联数据处理: adassociative data processing安全关联数据库: sadb数据库方法: data base method关联: relevance; be related; correlation; relevancy; interaction; interconnection; be connected 互相关联的 be interrelated; 数学和天文学是互相关联的科学。 mathematics and astronomy are cognate sciences.; 关联词 conjunctive word; 关联 [互联]系统 interconnected [interacted] system; 关联现象 correlation; 关联性 relevance关联式数据库管理系统: rdbms relational database management system; rdbms, relational database management system关联式资料库 [关系数据库: relational database数据编码方法: data encoding scheme; data-encoding scheme