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稳态kalman滤波
相关语句
  steady-state kalman filtering
     Using steady-state Kalman filtering theory, a multi-sensor optimal information fusion steady-state Kalman filter is given based on this fusion criterion.
     运用稳态Kalman滤波理论,基于该融合准则,给出了多传感器最优信息融合稳态Kalman滤波器.
短句来源
     Based on classical steady-state Kalman filtering theory, a new approach of designing optimal Wiener state estimators is presented for the system with white and colored observation noises.
     基于经典稳态Kalman滤波理论,对带白色和有色观测噪声系统提出了设计最优Wiener状态估值器的新方法。
短句来源
     Using steady-state Kalman filtering theory, a fixed-interval Wiener smoother was presented, the recursive version of non-recursive steady-state optimal fixed-interval Kalman smoother yielded the fixed-interval Wiener smoother.
     应用稳态Kalman滤波理论,提出了一种固定区间Wiener平滑器,由稳态最优非递推固定区间Kalman平滑器的递推变形引出固定区间Wiener平滑器.
短句来源
     Using classical steady-state Kalman filtering theory, a new approach of designing Wiener state estimators is presented, whose principle is that based on steady-state Kalman filter and predictor given in the Wiener filter form, and using the autoregressive moving average (ARMA) innovation model, the recursive version of non-recursive steady-state optimal state estimators yields the Wiener state estimators.
     应用经典稳态Kalman滤波理论提出了设计Wiener状态估值器的新方法,其原理是:基于在Wiener滤波器形式下的稳态Kalman滤波器和预报器及ARMA新息模型,由稳态最优非递推状态估值器的递推变形引出Wiener状态估值器.
短句来源
     Functional Equivalence of Two Measurement Fusion Methods Based on <font color=red >Steady-state Kalman Filtering</font> </td></tr> <tr><td class="text11Green">     基于<font color=red >稳态Kalman滤波</font>的两种观测融合方法的功能等价性 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KXJS200411003.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-KXJS200411003.html')">短句来源</a></td></tr> <tr><td align="right"><a href="dict_more_sen.aspx?scw=%e7%a8%b3%e6%80%81kalman%e6%bb%a4%e6%b3%a2&c=8&z=&tran=steady-state+kalman+filtering" target="_blank">更多</a>       </td></tr></TABLE> <TABLE width="100%"><tr><td><IMG id="j_1" style="cursor:pointer" onclick="showjds('showjd_1',this)" src="images/jian.gif" border="0">  <font size="3"><b><a href="javascript:showjdsw('showjd_1','j_1')" >steady-state kalman filter</a></b></font></td></tr></TABLE> <TABLE width="100%" id="showjd_1"> <tr><td>     ESTIMATION OF <font color=red >STEADY-STATE KALMAN FILTER</font> GAIN </td></tr> <tr><td class="text11Green">     <font color=red >稳态Kalman滤波</font>增益的估计 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KZLY198501014.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-KZLY198501014.html')">短句来源</a></td></tr> <tr><td>     TWO NEW ALGORITHMS FOR ESTIMATION OF <font color=red >STEADY-STATE KALMAN FILTER</font> GAIN AND THEIR APPLICATIONS </td></tr> <tr><td class="text11Green">     <font color=red >稳态Kalman滤波</font>增益估计的两种新算法及其应用 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-XXYK199106003.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-XXYK199106003.html')">短句来源</a></td></tr> <tr><td>     Using <font color=red >steady-state Kalman filter</font>ing theory, a multi-sensor optimal information fusion <font color=red >steady-state Kalman filter</font> is given based on this fusion criterion. </td></tr> <tr><td class="text11Green">     运用<font color=red >稳态Kalman滤波</font>理论,基于该融合准则,给出了多传感器最优信息融合<font color=red >稳态Kalman滤波</font>器. </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KZYC200402020.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-KZYC200402020.html')">短句来源</a></td></tr> <tr><td>     Furthermore, steady-state information fusion Kalman multi-step predictor is also given when <font color=red >steady-state Kalman filter</font> exists for each sensor systems. </td></tr> <tr><td class="text11Green">     当各传感器子系统存在<font color=red >稳态Kalman滤波</font>时,又给出了稳态信息融合Kalman多步预报器。 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KXJS200606002.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-KXJS200606002.html')">短句来源</a></td></tr> <tr><td>     Using classical <font color=red >steady-state Kalman filter</font>ing theory, a new approach of designing Wiener state estimators is presented, whose principle is that based on <font color=red >steady-state Kalman filter</font> and predictor given in the Wiener filter form, and using the autoregressive moving average (ARMA) innovation model, the recursive version of non-recursive steady-state optimal state estimators yields the Wiener state estimators. </td></tr> <tr><td class="text11Green">     应用经典<font color=red >稳态Kalman滤波</font>理论提出了设计Wiener状态估值器的新方法,其原理是:基于在Wiener滤波器形式下的<font color=red >稳态Kalman滤波</font>器和预报器及ARMA新息模型,由稳态最优非递推状态估值器的递推变形引出Wiener状态估值器. </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-MOTO200401015.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-MOTO200401015.html')">短句来源</a></td></tr> <tr><td align="right"><a href="dict_more_sen.aspx?scw=%e7%a8%b3%e6%80%81kalman%e6%bb%a4%e6%b3%a2&c=6&z=&tran=steady-state+kalman+filter" target="_blank">更多</a>       </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')" >“稳态kalman滤波”译为未确定词的双语例句</a></b></font></td></tr></TABLE> <TABLE width="100%" id="showjd_2"> <tr><td>     A numerical storm surge forecast model with Kalman filter </td></tr> <tr><td class="text11Green">     一个<font color=red >稳态Kalman滤波</font>风暴潮数值预报模式 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-SEAC200205003.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-SEAC200205003.html')">短句来源</a></td></tr> <tr><td>     1. Firstly introduced the theories such as State Space Model Method, Kalman Filter, Kalman Precursor, Kalman Smoother, The most superior smooth fixed sector, Stable state of Kalman Filter, and Edges the stability. </td></tr> <tr><td class="text11Green">     1、首先介绍了状态空间模型、Kalman滤波器、Kalman预报器、Kalman平滑器、最优固定区间平滑、<font color=red >稳态Kalman滤波</font>及其渐近稳定性等理论。 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CMFD-2007047244.nh.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CMFD-2007047244.nh.html')">短句来源</a></td></tr> <tr><td>     From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique. </td></tr> <tr><td class="text11Green">     本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术. </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-XXYK199106003.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-XXYK199106003.html')">短句来源</a></td></tr> <tr><td>     Using the modern time series analysis method, a unifying framework of steady state Kalman filtering, smoothing and prediction for singular discrete linear stochastic systems, is presented. A new algorithm of steady state Kalman estimator gain is given, where the solution of the Riccati equation is avoided. In order to ensure the asymptotic stability of the estimator, a formula of setting the initial estimate is given. </td></tr> <tr><td class="text11Green">     用现代时间序列分析方法,提出了广义离散线性随机系统<font color=red >稳态Kalman滤波</font>、平滑和预报的一种统一格式,给出了稳态Kalman估值器增益新算法,避免了求解Riccati方程.为保证估值器的渐近稳定性,给出了选择初始估值的公式.仿真例子说明了所提出的结果的有效性. </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-MOTO904.006.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-MOTO904.006.html')">短句来源</a></td></tr> <tr><td>     With the least variance characteristic of Kalman filter, the relation between the variance of Kalman filter and intensities of model noise is first analyzed under the condition of a given measurement noise, then an LMI approach based solution to steady current estimation type filter is presented when both intensities of model noise and measurement noise are fixed. </td></tr> <tr><td class="text11Green">     根据 Kalman滤波的最小方差特性 ,分析测量噪声一定时 Kalman滤波方差随系统模型噪声强度变化的规律 ,并在模型噪声和测量噪声一定时 ,给出求解当前估计型稳态 Kalm an滤波的 L MI方法。 </td></tr> <tr><td class="text11" align="right"> <a href="http://xuewen.cnki.net/CJFD-KZYC200105008.html" target="_blank" onclick="record('稳态kalman滤波', '双语例句', 'http://xuewen.cnki.net/CJFD-KZYC200105008.html')">短句来源</a></td></tr> <tr><td align="right"><a href="dict_more_sen.aspx?scw=%e7%a8%b3%e6%80%81kalman%e6%bb%a4%e6%b3%a2&c=7&z=&unvsm=1" target="_blank">更多</a>       </td></tr></TABLE> </td></tr><tr><td> <IMG src="images/userdefine.png" border="0"> <font color="blue" size="3"><b>查询“稳态kalman滤波”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('%u7a33%u6001kalman%u6ee4%u6ce2');return false;}">的双语例句 <input style="cursor:pointer;" type="button" name="Submit" value="提交" onclick="tjCustom('%u7a33%u6001kalman%u6ee4%u6ce2');"></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><table width="100%"><tr><td><font class="text6">为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。</font></td></tr><tr><td><table width="100%"><tr><td><IMG id="lj_0" style="cursor:pointer" onclick="showjds('showlj_0',this)" src="images/jian.gif" border="0">  <font color="blue" size="3"><b><a href="javascript:showjdsw('showlj_0','lj_0')" >steady-state kalman filtering</b></font></td></tr></table><table width="100%" id="showlj_0"><tr><td> Haddad, <font color=red >Steady-state Kalman filtering</font> with an H error bound, Systems Control Lett. </td></tr> <tr><td align="right">      </td></tr></table><table width="100%"><tr><td><IMG id="lj_1" style="cursor:pointer" onclick="showjds('showlj_1',this)" src="images/jian.gif" border="0">  <font color="blue" size="3"><b><a href="javascript:showjdsw('showlj_1','lj_1')" >steady-state kalman filter</b></font></td></tr></table><table width="100%" id="showlj_1"><tr><td> An ensemble Kalman filter-based <font color=red >steady-state Kalman filter</font> is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly non-linear stratified shallow bay. </td></tr> <tr><td align="right">      </td></tr><tr><td> It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a <font color=red >steady-state Kalman filter</font>. </td></tr> <tr><td align="right">      </td></tr></table></td></tr></table></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"> This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems. It consists of three parts; (i) An ARMAX innovation model is derived using Fadeeva's scheme for computing inverse miatrix; (ii) the moving average parameter matrices in ARMAX innovation model are identified by using Gevers and Wouters' algorithm which ensures the invertability of the innovation model; (iii) a new algorithm for estimating the steady -state filter gain is given, which is simpler than... </p><p class="wz wz-en-all">This paper presents a new method for estimating the steady-state Kalman filter gain for linear discrete systems. It consists of three parts; (i) An ARMAX innovation model is derived using Fadeeva's scheme for computing inverse miatrix; (ii) the moving average parameter matrices in ARMAX innovation model are identified by using Gevers and Wouters' algorithm which ensures the invertability of the innovation model; (iii) a new algorithm for estimating the steady -state filter gain is given, which is simpler than Tajima's.</p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">本文提出了求线性离散系统<font color=red >稳态Kalman滤波</font>增益的新方法。它由三部份组成:(ⅰ)基于矩阵求逆的Fadeeva公式导出了ARMAX新息模型。(ⅱ)用Gevers和Wouters算法辨识ARMAX新息模型中的滑动平均部份的参数阵,可保证新息模型的可逆性。(ⅲ)给出了一种比Tajima算法更简单的估计稳态滤波增益的算法。</p></TD></TR><TR><TD class="text6"><p class="wz wz-en"> In this paper the structure identification and parameter estimation of MIMO linear discrete stochastic systems are discussed. Using Luenberger's observable canonical form with steady-state Kalman filtering representation, an online identification method is developed. The structural identification of the system is determined with the residual error method by developing a recursive algorithm. Parameter estimation is made with the recursive extended instrumental variable method which gives an asymptotically Unbiased... </p><p class="wz wz-en-all">In this paper the structure identification and parameter estimation of MIMO linear discrete stochastic systems are discussed. Using Luenberger's observable canonical form with steady-state Kalman filtering representation, an online identification method is developed. The structural identification of the system is determined with the residual error method by developing a recursive algorithm. Parameter estimation is made with the recursive extended instrumental variable method which gives an asymptotically Unbiased estimate. A recursive algorithm for the residual-square-sum is thus obtained. Computer simulation regults indicate that the proposed method is sufficiently good to be used for adaptive Control purposes.</p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">本文讨论多输入多输出(MIMO)随机线性离散系统的在线测辨问题。根据Luenberger标准型的<font color=red >稳态Kalman滤波</font>表示提出了一套在线测辨方法,即用残差法的结构测辨和递推增广工具变量法(REIV)的参数估计。本文给出了残差平方和的递推算式,并证明了如果采用本文所提出的工具变量,那末REIV算法是渐近无偏的。最后,我们还给出了计算机模拟算例,说明本文所提出的在线测辨算法是行之有效的。</p></TD></TR><TR><TD class="text6"><p class="wz wz-en"> From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.New algorithms are simpler than that of Mehra and Tajima.As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms. </p></TD></TR><TR><TD class="text6"><p class="wz wz-zh">本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.新算法比Mehra 和 Tajima 的算法简单.作为应用例子,对于一个简单的跟踪系统,导出了带输入估计的自校正α-β滤波器,仿真结果说明了新算法的有效性.</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=&scw=%e7%a8%b3%e6%80%81kalman%e6%bb%a4%e6%b3%a2&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_1937071000.html">稳态kalman滤波器</a></li><li><a href="h_8918000.html">稳态</a></li><li><a href="h_52323163000.html">kalman</a></li><li><a href="h_1804676000.html">稳态kalman估值器</a></li><li><a href="h_2251327000.html">稳态最优kalman平滑</a></li><li><a href="h_2900525000.html">稳态kalman滤波器增益</a></li><li><a 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