kalman filter kalman filtering 中文意思是什麼

kalman filter kalman filtering 解釋
卡門濾波
  • kalman : 卡爾曼
  • filter : n 1 濾器，濾紙，過濾用料[砂、炭等]。2 【無線電】濾波器；【物理學】濾光鏡，濾色器。vt 過濾，用過濾...
  1. By changing the multiple time - varying fading factors of the strong tracking filter, mstkf switches between kalman filtering and strong tracking filtering

    通過直接改變強跟蹤濾波器的多重時變漸消因子， mstkf在卡爾曼濾波和強跟蹤濾波兩種工作狀態之間切換。
  2. 4. suboptimal fading ddf ( sfddf ) and suboptimal fading ukf ( sfukf ) algorithm can be obtained with the application of suboptimal fading ekf. suboptimal fading algorithm is an error compensative technology basing on the kalman filter orthogonality principle, which can greatly improve filtering precision and stability

    4 、把一種帶次優漸消因子的ekf演算法推廣到ddf和ukf演算法中，得到sfddf和sfukf演算法，次優漸消因子是一種基於kalman濾波正交原理誤差補償技術，它的引入較大的改善了濾波精度和演算法穩定性。
  3. Then this paper introduced the main method in multi - sensor integrated navigation - kalman filtering method, and a two - level optimization multi - sensor information fusion structure - combined filter which was originated by carlson and kerr, based on the structure of combined filter, it studied the method of navigating by the multi - sensor navigation system integrated by ins milemeter altimeter and piloting, then analyzed the effect of several filters. simulation proved that when altimeter were integrated, the height error was reduced a lot, and the combined filter is more effective than one - level kalman filter

    然後，介紹了組合導航中的關鍵技術? ?卡爾曼濾波方法，以及一種二級最優多傳感器融合結構? ? carlson ， kerr等人提出的聯合濾波器，並以聯合濾波器的結構為基礎研究了車載捷聯慣導系統與里程計、氣壓高度計、地標組合導航的方法，比較了幾種組合方法的效果。模擬結果表明，引入氣壓高度計可以有效的減小高度誤差，二級聯合濾波器的效果優於一級結構的卡爾曼濾波器。
  4. Compared with kalman filtering, the unknown definite disturbance with finite energy instead of white noise drives the state - space system in h filtering. compared with the time - variant filter and the first - order filter, h filter has preferable robustness

    與kalman濾波相比， h _濾波採用未知的具有有限能量的確定性干擾代替白噪聲驅動狀態空間系統；與時變濾波器和一階濾波器相比， h _濾波器具有較強的魯棒性。
  5. Applications of their to self - tuning filtering are given, where the steady - state kalman tracking filter with the position and velocity measurements, and multivariable self - tuning tracking predictor, filter and smoother are presented

    給出了它們在自校正濾波中的一些應用。其中包括帶位置和速度觀測的穩態kalman跟蹤濾波器和多變量自校正跟蹤預報器、濾波器和平滑器。
  6. Because the ins error equation is unstable, some initial states error will cause error floating and error accumulating, if the filter observations were only position error, kalman filter will converge very slowly, and some states error ( such as yaw error ) will be great. since the milemeter altimeter and piloting could only output position information, this paper put forward a method, firstly estimateing states and then kalman filtering, to improve filtering effect. simulation proved that this method could effectively reduce the system states error, quicken filtering convergence and improve filtering precision

    由於慣導系統（ lsins ）的誤差方程是發散的，某些初始狀態的誤差會引起誤差的漂移和積累，當觀測量只有位置誤差時，卡爾曼濾波的收斂速度很慢，某些狀態（如方位角）誤差很大，而以上除慣導外的其它導航傳感器直接提供的只是位置信息，為了改善濾波器性能，本文根據里程計等傳感器的特點，提出了首先對狀態做出估計，然後在狀態估計的基礎上，進行卡爾曼濾波的方法。
  7. Recently, withthe rapid improvement of performance of digital processor, sequential monte carlo ( smc ) method has a wide range of application in engineering, especially in signal processing, statistics, and econometrics etc. the time varying systems can be stated in the form of a dynamic state space model. for linear models and gaussian noise, the kalman filter provides analytical expressions for posterior filtering

    一般的時變系統都可以被看作是一動態狀態空間模型，對于線性高斯模型，卡爾曼濾波可以給出后驗密度函數的解析解；而對于非線性非高斯模型，我們則無法得到它的解析解，在這種情況下則可以使用序列蒙特卡羅方法來對其進行近似。
  8. Hence, the kalman filter gain matrix can be computed off - line using matlab. the research uses compensating method to solve kalman filtering divergence problems

    針對計算誤差引起的濾波發散問題，本文採用性能退化參數補償法，初步解決了濾波的發散問題。
  9. Based on above performances the applications of multi - sensor data fusion in state estimation for maneuvering target is studied systemically. the main work includes : based on the analysis that the extreme value of acceleration presupposed causes influence in the “ current ” statistical model, a modified model is given, which utilizes the functional relationship between maneuvering status and estimation of the neighboring intersample position vector to carry out the self - adaptive of the process noise variance. then combining with the recursive characteristic of kalman filter, an improved self - adaptive filtering algorithm is presented

    基於此，本文針對多傳感器數據融合技術在機動目標狀態估計中的應用進行了系統的研究，其主要工作如下: 1 、基於「當前」統計模型中加速度極限值的預先設定對于濾波效果影響的分析，利用目標機動狀況與相鄰采樣時刻間位置估計量變化之間的函數關系實現噪聲方差自適應，進而提出了一種修正的模型，並結合卡爾曼濾波遞推演算法，提出了一種改進的自適應濾波演算法。
  10. This paper presents and probes into several filtering schemes for the deep space explores attitude measurement system composed of star sensors and gyros. under the stellar - inertial modes, two attitude determination algorithms are designed which use the extended kalman filter. one of the algorithms is to linearized the state equation based on the optimal estimation

    本文中以星敏感器和光纖陀螺為基本配置組成的深空探測器姿態測量系統為對象，針對星敏感器與光纖陀螺聯合定姿模式和基於星敏感器的定姿模式，對深空探測器的三軸姿態濾波技術方案進行了設計與研究。
  11. Finally, we discuss application of kalman filtering. the optimality of multi - sensor kalman filtering fusion with feedback is presented and a filter bank based on wavelets and equipped with a miltiscale kalman filter is proposed for estimating fractal signal in additive gaussian white noise

    最後，我們討論了卡爾曼濾波在實際中的應用，分析了多傳感器反饋卡爾曼濾波融合的最優性，並且基於小波變換利用卡爾曼濾波對淹沒在高斯白噪聲中的分形信號進行了波形估計。
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