完整后设资料纪录
DC 栏位语言
dc.contributor.author古锦安en_US
dc.contributor.authorGU, JING-ZNen_US
dc.contributor.author魏哲和en_US
dc.contributor.authorWEI, ZHE-HEen_US
dc.date.accessioned2014-12-12T02:08:31Z-
dc.date.available2014-12-12T02:08:31Z-
dc.date.issued1990en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT792430015en_US
dc.identifier.urihttp://hdl.handle.net/11536/55358-
dc.description.abstractWhen a maneuvering target is tracked by a radar system at high measurement
frequency, the measurement noise is usually highly correlated and is often
modeled as a first-order Markov process. By generating the artificial
measurement and reformulating the measurement equation, some efficient
decorrelation algorithms can be derived corresponding to several
maneuvering target tracking methods: the state augmentation method, the
maneuver detection method, the input estimation method, the Gholson's
multiple model method, and the interacting multiple model method. If the
measurement noise is complicated and is modeled as the sum of a high-order
autoregressive process and a white process, then, by adding some
noise-correlation variables to the target state, the decorrelation process
can also be completed. The tracking performances with and without
decorrelation are carefully evaluated by theoretic analysis or computer
simulation results. It can be found that significant improvement,
particularly in velocity and acceleration estimations, can be provided by
the decorrelation process.
If some of the parameters including the noise-correlation parameters are
undnown, these parameters should be estimated before the decorrelation
process starts to work. If the state augmentation method is employed for
tracking the target, the autocorrelations of the innovation can be
obtained as functions of the unknown parameters. Then, using this
relationship and taking the time-average autocorrelations to approximate
the statistical autocorrelations, the unknown parameters can be estimated
adaptively. A modified structure denoted as 'multiple level estimator'
may provide good performance in estimating the parameters with efficient
computation. If the maneuver detection method or the input estimation
method is employed for tracking, a simple (approximate) parameter
estimation technique is available.
高取样率雷达系统追踪一战术运动目标时,其测量杂讯常呈相当的关联性而可模式
化为一阶的马可夫过程。对应于几种常见的战术运动目标追踪技巧:状态扩充法,
战术运动侦测法,输入估计法,高尔森多重模式法及交连式多重模式法,可利用产
生人工测量值的过程,重新推导测量方程式,而得到几种有用的消除杂讯关性的方
法。如困测量杂讯相当复杂,而被模式化为一高阶自动递回过程和一白色过程之和
,则将杂讯关联性变数并入目标状态考量,消除杂讯关联性的作用也可以完成。在
本论文里,消除杂讯关联性前后的追踪效困均以理论分析或计算机模拟作了详细的
评估与比较。我们可发现,经过消除杂讯关联性的处理,追踪效困得到重大的改善
,特别在对目标速度及加速度的估计上。
如困包括杂讯关联性参数的某些参数为未知,则在执行消除杂讯关联性处理之前,
这些未佑参数宜先估计出来。若我们用状态扩充法追踪目标,则推导出测量值创新
处理的自相关函数为未佑参数的函数,并取自相关函数的时间平均值代替统计值,
可将这些参数适应性地估计出来。本论文里还提出一个取名为〞多重层级估计法〞
的修正式结构,可以有效地计算参数而达到良好估计效果。此外,如果以戢术运动
侦测法或输入估计法来追踪目标,参数估计可以用一简化的近似法求得。
zh_TW
dc.language.isozh_TWen_US
dc.subject战数运动目标zh_TW
dc.subject高取样率zh_TW
dc.subject马可夫过程zh_TW
dc.subject状态扩充法zh_TW
dc.subject高尔森多重模式法zh_TW
dc.subjectMANEUVERING-TARGETen_US
dc.subjectHIGH-MEASUREMENT-FREQUENCYen_US
dc.subjectMARKOV-PROCESSen_US
dc.subjectTHE-STATE-AUGMENTATION-METHODen_US
dc.subjectGMDMen_US
dc.title高取样率雷达系数对战术运动目标的追踪技巧zh_TW
dc.titleTRACKING TECHNIQUES FOR MANEUVERING TARGET AT HIGH MEASUREMENT FREQUENCYen_US
dc.typeThesisen_US
dc.contributor.department电子研究所zh_TW
显示于类别:Thesis