kalman filter 中文意思是什麼
kalman filter
解釋
卡爾曼濾波器-
Because it is impossible to adjust the air bearing absolutely balance, the difference between mass center and rotation center must lead to a notable gravity disturbance torque. in the process of identification, modeling and identification of gravity disturbance torque is considered, and an extended kalman filter is educed for identifying air bearing inertia matrix and gravity disturbance torque, and then the algorithm is validated
由於氣浮臺平衡調試方法和手段的限制，實驗過程中氣浮臺質心與轉動中心有一定的位置偏差，必將導致較大的重力干擾力矩，在辨識演算法的推導過程中，重點考慮了重力干擾力矩的建模和參數辨識問題，推導了氣浮臺的轉動慣量和重力干擾力矩辨識的擴展卡爾曼濾波演算法，並對演算法進行了數學模擬和全物理模擬驗證。 -
The human motion tracking section discusses the use of combined algorithm to prevent kalman filter from diverge, while to estimate the motion state of human in image sequence accurately. when people mutually occlude forming group and then split, the algorithm in this paper matches human based on their color feature, resolves the problem to match human before forming group and after group splitting, and achieves satisfied results
在單獨人體融合成人群然後人群再分裂成單獨人體的情況下，本文的演算法利用人體的顏色特徵進行人體之間的匹配，解決了融合成人群前的人體與人群分裂后的人體之間的對應問題，並在人體運動跟蹤中取得了滿意的效果。 -
In this paper, the main research works are as follows : 1 ) survey and analysis previous work in auv navigation and summarize : as the cumulate error, the dead - reckoning and inertial navigation systems ca n ' t be used without other modified system ; the acoustic navigation system is used only in small range, and is very expensive. 2 ) two important approaches in geophysical navigation techniques are summarized, one is terrain contour based navigation, this approach is investigated keystone today ; the other is image based navigation, due to no perfect image sensors and image seabed map, this approach has seldom been practised. 3 ) two important terrain contour based navigation algorithms has been applied to auv, one is match algorithm - based terrain contour match ( tercom ) ; the other is kalman filter - based sandia inertia terrain - aided navigation ( sitan )
主要研究內容包括：一、總結分析了當前水下導航的常用方法和不足之處，指出基於航位推算的導航方法，存在著最大的問題就是導航誤差的積累問題，如果沒有修正系統，這種方法很難滿足使用要求；其次指出基於聲學的導航方法，造價昂貴，導航范圍有限，難以滿足自主水下航行器大范圍導航的需求；二、總結了當前水下地形輔助導航的主要技術路線，一是基於地形高程的匹配方法，這是當前研究的重點；二是基於地形圖像的匹配方法，由於缺乏良好的圖像傳感器，和可用的海底圖像地圖，目前這種方法還有待研究發展；三、將兩種地形高程匹配的常用方法應用到自主水下航行器上：以相關演算法為前提的地形輪廓匹配（ tercom ）方法和以卡爾曼濾波為基礎的桑迪亞慣性地形輔助導航（ sitan ）方法，前者演算法簡單可靠，但是導航精度不高，後者雖然精度比較高，但存在著演算法較為復雜、有可能發散和出現奇異值等問題。 -
Kalman filter is the best estimate under the linear and unprejudiced least mean square error rule
卡爾曼濾波是線性無偏最小方差準則下的最優估計。 -
Aiming at the nonlinearity of state and measure equation and measurement being only angle information, which results in poor observability and classical estimate methods such as extended kalman filter not converging, the boost phase states and covariance are estimated using unscented kalman filter ( ukf )
針對狀態方程和觀測方程都是非線性方程，觀測量只有角度信息，造成可觀測性弱、經典的濾波演算法如擴展卡爾曼濾波方法不易收斂的問題，利用unscented卡爾曼濾波ukf演算法對主動段進行狀態和協方差估計。 -
The equations of the mean value functions and the covariance functions are established for dynamical systems whose inputs are fuzzy stochastic processes. an existence and uniqueness theorem of ito fuzzy stochastic differential equations is proved, some explicit representations of solutions and the equations of statistical characteristics are deduced for linear fuzzy stochastic differential equations, and numerical methods to nonlinear fuzzy stochastic differential equations are proposed, the conditions for stability and observability of fuzzy linear systems are derived. the kalman filter algorithms of linear fuzzy stochastic systems are brought forward
主要成果包括：提出了模糊隨機變量協方差和反向協方差的概念；研究了二階模糊隨機變量的均方收斂性，並在此基礎上得到了均方模糊隨機分析、平穩模糊隨機過程及其譜分解的若干定理；根據均方模糊隨機分析理論，得到了輸入為模糊隨機過程的線性系統的輸出輸入統計特徵關系方程；證明了ito型模糊隨機微分方程解的存在唯一性，並給出了ito型線性模糊隨機微分方程解的表達式，統計特徵方程以及非線性模糊隨機微分方程的數值解法；得到了模糊線性系統的穩定性和可觀性條件、線性模糊隨機系統統計特徵方程和線性模糊隨機系統的kalman濾波演算法；研究了當觀測值是模糊數據時，線性回歸模型的建立。 -
Combining with the performances of danshan bridge, discussing the methods on the construction controlling and calculation of cable - stayed bridge, this paper raises an efficient construction controlling system, and realizes collecting construction data of the real bridge and technical information. by analyzing on the difference of theoretical calculation and real construction state, the author determines to take self - adapting construction controlling method which uses kalman filter method to identify and adjust construction parameters. by getting rational construction state from forward iteration method, this paper, taking concrete creep effect in consideration, raises a rational scheme to pre - remain deformation and adjust cable force
結合丹山橋的結構特點，通過對斜拉橋施工控制及結構計算方法的探討，提出了有效的施工控制系統，實現了對實橋施工數據和技術信息的採集；通過分析理論計算與實際施工狀態的差異，確定了以卡爾曼濾波法作為施工參數識別與調整的自適應施工控制方法；通過正裝迭代法確定其合理施工狀態，並考慮混凝土的徐變效應，提出了合理的預留拱度和索力調整方案。 -
The extended kalman filter ( ekf ) and converted measurement kalman filter ( cmkf ) have been widely used in radar target tracking
在非線性量測的情況下， ekf和cmkf得到了廣泛的應用。 -
When the targets maneuver slightly, we can get good result using extended kalman filter ( ekf )
當目標做輕微機動時，將機動加速度看作噪聲採用擴展卡爾曼濾波（ ekf ）可以得到很好的跟蹤效果。 -
Combining kalman filter with fuzzy algorithm, the paper also develops a method for tracking moving targets at a long rang
此外，本文將kalman濾波與模糊演算法相結合提出了一種小目標跟蹤演算法。 -
( 4 ) applying measurement noise automatic weighting kalman filter to the guidance of line - guidance torpedo, we compromised target information and torpedo information, which in turn come from sonar of guidance station and inertial measurement units of torpedo
（ 4 ）將多傳感器量測噪聲自動加權卡爾曼濾波應用在線導魚雷制導中，融合來自製導站聲納測量到的目標信息和來自魚雷慣性測量組件的魚雷位置和姿態等信息進行了模擬研究。 -
Kalman filter is an optimal filter algorithm in the minimum - mean - square - error sense, meanwhile extended kalman filter is a sub - optimal filter algorithm, which derived from the linearization of nonlinear system using taylor expansion. while the non - linearity of the system is not extreme strong, ekf can achieve approximately optimal filter effect
Kf是最小均方意義下的最優濾波演算法， ekf則是利用一階泰勒展開將非線性系統線性化而得到的一種次優濾波演算法，在非線性不是特別嚴重的情況下， ekf有著近似最優的濾波效果。 -
The fifth chapter is about the design and realization of kalman filter in the system
第五章討論了指揮車系統中卡爾曼跟蹤濾波器。 -
The unscented kalman filter is used to estimate the probe position, velocity and un - model acceleration, which improves the orbit estimation accuracy and ensures the stability of navigation algorithm
數學模擬結果表明，提出的導航方法是可行的，能夠滿足月球探測器奔月與繞月軌道段導航精度的要求。 -
The target movement information which aa gunshot fire control needs can he provided by using kalman filter to process the dada offered by on hoard fire control radar and other sensors
摘要使用卡爾曼濾波器對機載火控雷達及其它傳感器提供的數據進行處理，可以得到空對空射擊火力控制需要的目標運動信息。 -
2. using the startlight angle as measurements, extended kalman filter is used for orbit determination
以星光角距為量測量，運用廣義卡爾曼濾波實現定軌。 -
And in this foundation, unified the kalman filter to make the improvement to the carrier recover algorithm, enhanced the synchronous system performance
並在此基礎上，結合卡爾曼濾波器對載波同步演算法作了改進，提高同步系統性能。 -
The kalman filter is introduced and explored by assimilating sea temperature observations into one - dimensional numerical temperature model, which is decomposed into two parts, one part is the certain, the other is the random. then a data assimilation model is set up, it can assimilate sea temperature continuous observations data
在此基礎上，利用kalman濾波原理，以一維海溫垂直分佈的數值模式為例，通過對原模式進行分解，得到確定性模式和隨機過程兩部分，繼而建立了一個可同化海溫連續觀測資料的數據同化模式。 -
Furthermore a distributed kalman filter is developed. in this algorithm, kalman filter is divided into three independent parts, which is processed at the same timed
將遞推過程分為相對獨立的三個部分，可以同時分別計算，大大加快了演算法速度。 -
Utilizing control lyapunov function, the position control and trajectory control law are designed for the robot. using matlab and simulink, simulation results are provided to show the efficacy of the kalman filter and controllers. also, multi - sensor data fusion, balancing control, robustness control and velocity tracking experiments are conducted to ensure the validity and efficacy
利用matlab及simulink對上述濾波器、控制器進行模擬，並通過兩輪自平衡機器人系統實驗平臺進行數據融合、平衡控制、魯棒性、速度跟蹤實驗，獲得了期望的平衡與速度跟蹤性能，驗證了數據融合演算法和相應控制器的正確性與有效性。
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