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kalman跟踪滤波器
相关语句
  kalman tracking filter
     Multisensor information fusion steady-state Kalman tracking filter
     多传感器信息融合稳态Kalman跟踪滤波器
     Weighted measurement fusion Kalman tracking filter
     加权观测融合Kalman跟踪滤波器
     Information Fusion Kalman Tracking Filter with Position and Velocity Measurements
     带位置和速度观测的信息融合Kalman跟踪滤波器
短句来源
     Multi-sensor information fusion steady-state Kalman tracking filter with colored measurement noise
     带有色观测噪声多传感器信息融合稳态Kalman跟踪滤波器
短句来源
     Based on online parameter estimation of the ARMA. innovation models, using the modern time series analysis method, the several self-tuning Kalman tracking filters are presented, where the three different algorithms of the Kalman tracking filter gains are used.
     基于ARMA新息模型参数的在线估计,应用现代时间序列分析的方法,提出了若干自校正KALMAN跟踪滤波器,其中,应用了求KALMAN跟踪滤波器稳态增益的三种不同算法。
短句来源
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  “kalman跟踪滤波器”译为未确定词的双语例句
     uivalence of Two Kinds of Kalman Tracking Filters Based on the ARMA Innovation Model and Riccati Equation </td></tr> <tr><td class="text11Green">     基于ARMA新息模型与Riccati方程的两种<font color=red >Kalman跟踪滤波器</font>的等价性 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KXJS200411002.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CJFD-KXJS200411002.html')">短句来源</a></td></tr> <tr><td>     Using the modern time series analysis method based on the autoregressive moving average (ARMA) model, and the Kalman filtering method based on Riccati equation, for the two-sensor system with the position and velocity measurements, under the linear minmum variance information fusion criterion, information fusion Kalman tracking filters weighted by matrices, diagonal matrices and scalars respectively are presented, where the information fusion Kalman filter weighted by scalars obviously reduces the computional burden and is suitable for real time applications. </td></tr> <tr><td class="text11Green">     应用基于ARMA模型的现代时间序列分析方法,和应用基于Riccati方程的经典Kalman滤波方法,对带位置和速度观测的两传感器系统,在线性最小方差信息融合准则下,分别提出了按矩阵加权、对角阵加权和标量加权的三种信息融合<font color=red >Kalman跟踪滤波器</font>,其中,按标量加权可明显减少计算负担,便于实时应用。 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KXJS200501003.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CJFD-KXJS200501003.html')">短句来源</a></td></tr> <tr><td>     For the target tracking systems with colored measurement noises,under the linear minimum variance information fusion criterion,three kinds of the multi-sensor information fusion steady-state Kalman filters weighted by matrices,diagonal matrices and scalars are obtained by two methods based on the ARMA innovation model and Riccati equation,respectively. </td></tr> <tr><td class="text11Green">     针对带有色观测噪声的目标跟踪系统,分别用基于ARMA新息模型和基于R iccati方程的两种方法,在线性最小方差信息融合准则下,提出了多传感器按矩阵加权、对角阵加权和标量加权的三种信息融合稳态<font color=red >Kalman跟踪滤波器</font>. </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-HLDZ200602008.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CJFD-HLDZ200602008.html')">短句来源</a></td></tr> <tr><td>     For the target tracking system with the position and velocity measurements, under the linear minimum variance information fusion criterion. three kinds of the multisensor information fusion steady-state Kalman tracking filters weighted by matrices. diagonal matrices and scalars are obtained by two methods based on the ARMA innovation model and based on Riccati equation, respectively. </td></tr> <tr><td class="text11Green">     针对带位置和速度观测的目标跟踪系统,分别用ARMA新息模型和基于Riccati方程两种方法,在线性最小方差信息融合准则下,提出了多传感器按矩阵加权、对角阵加权和标量加权的3种信息融合稳态<font color=red >Kalman跟踪滤波器</font>.通过一个仿真例子比较了它们的滤波误差,且验证了利用两种方法所得的结果相同. </td></tr> <tr><td class="text11" align="right"></td></tr> </TABLE> <TABLE width="100%"><tr><td><IMG id="j_2" style="cursor:pointer" onclick="showjds('showjd_2',this)" src="images/jian.gif" border="0">  <font size="3"><b><a href="javascript:showjdsw('showjd_2','j_2')" >相似匹配句对</a></b></font></td></tr></TABLE> <TABLE width="100%" id="showjd_2"> <tr><td>     Weighted measurement fusion Kalman tracking filter </td></tr> <tr><td class="text11Green">     加权观测融合<font color=red >Kalman</font><font color=red >跟踪</font><font color=red >滤波器</font> </td></tr> <tr><td class="text11" align="right"></td></tr> <tr><td>     Multisensor information fusion steady-state Kalman tracking filter </td></tr> <tr><td class="text11Green">     多传感器信息融合稳态<font color=red >Kalman</font><font color=red >跟踪</font><font color=red >滤波器</font> </td></tr> <tr><td class="text11" align="right"></td></tr> <tr><td>     3-D visual tracking based on CMAC neural network and Kalman filter </td></tr> <tr><td class="text11Green">     基于CMAC神经网络和<font color=red >Kalman</font><font color=red >滤波器</font>的三维视觉<font color=red >跟踪</font>(英文) </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-DNDY200301013.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CJFD-DNDY200301013.html')">短句来源</a></td></tr> <tr><td>     We tried Kalman filter, Meanshift for tracking purpose. </td></tr> <tr><td class="text11Green">     在<font color=red >跟踪</font>方面分别实验了<font color=red >Kalman</font><font color=red >滤波器</font>、Meanshift<font color=red >跟踪</font>方法。 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CMFD-2007057856.nh.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CMFD-2007057856.nh.html')">短句来源</a></td></tr> <tr><td>     The anti-jamming algorithm of Kalman filter </td></tr> <tr><td class="text11Green">     <font color=red >Kalman</font><font color=red >滤波器</font>的抗干扰算法 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-ZJGD200703008.html" target="_blank" onclick="record('kalman跟踪滤波器', '双语例句', 'http://xuewen.cnki.net/CJFD-ZJGD200703008.html')">短句来源</a></td></tr> </TABLE> </td></tr><tr><td> <IMG src="images/userdefine.png" border="0"> <font color="blue" size="3"><b>查询“kalman跟踪滤波器”译词为用户自定义的双语例句<br><br></b></font>    我想查看译文中含有:<input type="text" id="custom" name="custom" onkeydown="if(event.keyCode=='13'){tjCustom('kalman%u8ddf%u8e2a%u6ee4%u6ce2%u5668');return false;}">的双语例句 <input style="cursor:pointer;" type="button" name="Submit" value="提交" onclick="tjCustom('kalman%u8ddf%u8e2a%u6ee4%u6ce2%u5668');"></td></tr></TABLE></TD></TR> </TABLE><TABLE class=main-table cellPadding=0 cellSpacing=6 align=center><TR><TD><IMG src="images/dian_ywlj.gif" alt="例句" name=word></TD></TR><TR><TD><font class="text6Green">没有找到相关例句</font></TD></TR></TABLE><TABLE class=main-table cellPadding=0 cellSpacing=6 align=center><TBODY><TR><TD><IMG src="images/04.gif"><BR><BR></TD></TR><TR><TD class="text6"><p class="wz wz-en"> Using the modern time series analysis method based on the autoregressive moving average (ARMA) model, and the Kalman filtering method based on Riccati equation, for the two-sensor system with the position and velocity measurements, under the linear minmum variance information fusion criterion, information fusion Kalman tracking filters weighted by matrices, diagonal matrices and scalars respectively are presented, where the information fusion Kalman filter weighted by scalars obviously reduces the computional... </p><p class="wz wz-en-all">Using the modern time series analysis method based on the autoregressive moving average (ARMA) model, and the Kalman filtering method based on Riccati equation, for the two-sensor system with the position and velocity measurements, under the linear minmum variance information fusion criterion, information fusion Kalman tracking filters weighted by matrices, diagonal matrices and scalars respectively are presented, where the information fusion Kalman filter weighted by scalars obviously reduces the computional burden and is suitable for real time applications. A simulation example shows that two methods yield the same result, but a left-coprime factorization must be performed to construct the ARMA innovation model, and the accuracy difference of three fusion filters is not obvious.</p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">应用基于ARMA模型的现代时间序列分析方法,和应用基于Riccati方程的经典Kalman滤波方法,对带位置和速度观测的两传感器系统,在线性最小方差信息融合准则下,分别提出了按矩阵加权、对角阵加权和标量加权的三种信息融合<font color=red >Kalman跟踪滤波器</font>,其中,按标量加权可明显减少计算负担,便于实时应用。一个仿真例子说明了两种方法引出相同的结果,但构造ARMA新息模型时必须进行左素分解,且说明了三种加权融合滤波器的精度无显著差异。</p></TD></TR><TR><TD class="text6"><p class="wz wz-en"> For the target tracking systems with colored measurement noises,under the linear minimum variance information fusion criterion,three kinds of the multi-sensor information fusion steady-state Kalman filters weighted by matrices,diagonal matrices and scalars are obtained by two methods based on the ARMA innovation model and Riccati equation,respectively.Simulations show that the distinction between three filtering errors is not obvious,but the filter weighted by scalars reduces the computational burden and is... </p><p class="wz wz-en-all">For the target tracking systems with colored measurement noises,under the linear minimum variance information fusion criterion,three kinds of the multi-sensor information fusion steady-state Kalman filters weighted by matrices,diagonal matrices and scalars are obtained by two methods based on the ARMA innovation model and Riccati equation,respectively.Simulations show that the distinction between three filtering errors is not obvious,but the filter weighted by scalars reduces the computational burden and is suitable for real-time application.And it is verified that two methods yields the same result.Notice that constructing the ARMA innovation model,a left co-prime factorization to a polynomial matrix must be performed,so that the ARMA innovation model can correctly be obtained.</p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">针对带有色观测噪声的目标跟踪系统,分别用基于ARMA新息模型和基于R iccati方程的两种方法,在线性最小方差信息融合准则下,提出了多传感器按矩阵加权、对角阵加权和标量加权的三种信息融合稳态<font color=red >Kalman跟踪滤波器</font>.仿真说明了三种加权滤波器的误差的差别不明显,但按标量加权滤波器显著地减少了计算负担,便于实时应用,且验证了两种方法所得结果相同.应注意在构造ARMA新息模型时,必须进行多项式矩阵的左素分解,才能得到正确的ARMA新息模型.</p></TD></TR><TR><TD class="text6"><p class="wz wz-en"> >=For the target tracking system with the position and velocity measurements, under the linear minimum variance information fusion criterion. three kinds of the multisensor information fusion steady-state Kalman tracking filters weighted by matrices. diagonal matrices and scalars are obtained by two methods based on the ARMA innovation model and based on Riccati equation, respectively. Filtering errors are compared in the simulation example and the same result is get. </p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">针对带位置和速度观测的目标跟踪系统,分别用ARMA新息模型和基于Riccati方程两种方法,在线性最小方差信息融合准则下,提出了多传感器按矩阵加权、对角阵加权和标量加权的3种信息融合稳态<font color=red >Kalman跟踪滤波器</font>.通过一个仿真例子比较了它们的滤波误差,且验证了利用两种方法所得的结果相同.</p></TD></TR><TR><TD> <script type="text/javascript">getWzSwitch();</script></TD></TR><TR><TD align=right><a href="dict_result.aspx?m=m&style=&searchword=kalman%e8%b7%9f%e8%b8%aa%e6%bb%a4%e6%b3%a2%e5%99%a8&tjType=article" class="textlink9" )"><< 更多相关文摘</a>    </TD></TR></TBODY></TABLE><TABLE class=main-table cellPadding=0 cellSpacing=6 align=center><tr><td align=left class=text><img src="images/dot.gif" alt="图标索引" name=dot><strong> 相关查询</strong></td></tr><tr><td><div class="zztj"><ul><li><a href="h_943131000.html">kalman跟踪</a></li><li><a href="h_27051000.html">跟踪</a></li><li><a href="h_52323163000.html">kalman</a></li><li><a href="h_3396658000.html">信息融合kalman跟踪滤波器</a></li><li><a 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